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What is Scientific Research and How Can it be Done?

Scientific researches are studies that should be systematically planned before performing them. In this review, classification and description of scientific studies, planning stage randomisation and bias are explained.

Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new information is revealed with respect to diagnosis, treatment and reliability of applications. The purpose of this review is to provide information about the definition, classification and methodology of scientific research.

Before beginning the scientific research, the researcher should determine the subject, do planning and specify the methodology. In the Declaration of Helsinki, it is stated that ‘the primary purpose of medical researches on volunteers is to understand the reasons, development and effects of diseases and develop protective, diagnostic and therapeutic interventions (method, operation and therapies). Even the best proven interventions should be evaluated continuously by investigations with regard to reliability, effectiveness, efficiency, accessibility and quality’ ( 1 ).

The questions, methods of response to questions and difficulties in scientific research may vary, but the design and structure are generally the same ( 2 ).

Classification of Scientific Research

Scientific research can be classified in several ways. Classification can be made according to the data collection techniques based on causality, relationship with time and the medium through which they are applied.

• Observational
• Experimental
• Descriptive
• Retrospective
• Prospective
• Cross-sectional
• Social descriptive research ( 3 )

Another method is to classify the research according to its descriptive or analytical features. This review is written according to this classification method.

I. Descriptive research

• Case series
• Surveillance studies

II. Analytical research

• Observational studies: cohort, case control and cross- sectional research
• Interventional research: quasi-experimental and clinical research
• Case Report: it is the most common type of descriptive study. It is the examination of a single case having a different quality in the society, e.g. conducting general anaesthesia in a pregnant patient with mucopolysaccharidosis.
• Case Series: it is the description of repetitive cases having common features. For instance; case series involving interscapular pain related to neuraxial labour analgesia. Interestingly, malignant hyperthermia cases are not accepted as case series since they are rarely seen during historical development.
• Surveillance Studies: these are the results obtained from the databases that follow and record a health problem for a certain time, e.g. the surveillance of cross-infections during anaesthesia in the intensive care unit.

Moreover, some studies may be experimental. After the researcher intervenes, the researcher waits for the result, observes and obtains data. Experimental studies are, more often, in the form of clinical trials or laboratory animal trials ( 2 ).

Analytical observational research can be classified as cohort, case-control and cross-sectional studies.

Firstly, the participants are controlled with regard to the disease under investigation. Patients are excluded from the study. Healthy participants are evaluated with regard to the exposure to the effect. Then, the group (cohort) is followed-up for a sufficient period of time with respect to the occurrence of disease, and the progress of disease is studied. The risk of the healthy participants getting sick is considered an incident. In cohort studies, the risk of disease between the groups exposed and not exposed to the effect is calculated and rated. This rate is called relative risk. Relative risk indicates the strength of exposure to the effect on the disease.

Cohort research may be observational and experimental. The follow-up of patients prospectively is called a prospective cohort study . The results are obtained after the research starts. The researcher’s following-up of cohort subjects from a certain point towards the past is called a retrospective cohort study . Prospective cohort studies are more valuable than retrospective cohort studies: this is because in the former, the researcher observes and records the data. The researcher plans the study before the research and determines what data will be used. On the other hand, in retrospective studies, the research is made on recorded data: no new data can be added.

In fact, retrospective and prospective studies are not observational. They determine the relationship between the date on which the researcher has begun the study and the disease development period. The most critical disadvantage of this type of research is that if the follow-up period is long, participants may leave the study at their own behest or due to physical conditions. Cohort studies that begin after exposure and before disease development are called ambidirectional studies . Public healthcare studies generally fall within this group, e.g. lung cancer development in smokers.

• Case-Control Studies: these studies are retrospective cohort studies. They examine the cause and effect relationship from the effect to the cause. The detection or determination of data depends on the information recorded in the past. The researcher has no control over the data ( 2 ).

Cross-sectional studies are advantageous since they can be concluded relatively quickly. It may be difficult to obtain a reliable result from such studies for rare diseases ( 2 ).

Cross-sectional studies are characterised by timing. In such studies, the exposure and result are simultaneously evaluated. While cross-sectional studies are restrictedly used in studies involving anaesthesia (since the process of exposure is limited), they can be used in studies conducted in intensive care units.

• Quasi-Experimental Research: they are conducted in cases in which a quick result is requested and the participants or research areas cannot be randomised, e.g. giving hand-wash training and comparing the frequency of nosocomial infections before and after hand wash.
• Clinical Research: they are prospective studies carried out with a control group for the purpose of comparing the effect and value of an intervention in a clinical case. Clinical study and research have the same meaning. Drugs, invasive interventions, medical devices and operations, diets, physical therapy and diagnostic tools are relevant in this context ( 6 ).

Clinical studies are conducted by a responsible researcher, generally a physician. In the research team, there may be other healthcare staff besides physicians. Clinical studies may be financed by healthcare institutes, drug companies, academic medical centres, volunteer groups, physicians, healthcare service providers and other individuals. They may be conducted in several places including hospitals, universities, physicians’ offices and community clinics based on the researcher’s requirements. The participants are made aware of the duration of the study before their inclusion. Clinical studies should include the evaluation of recommendations (drug, device and surgical) for the treatment of a disease, syndrome or a comparison of one or more applications; finding different ways for recognition of a disease or case and prevention of their recurrence ( 7 ).

Clinical Research

In this review, clinical research is explained in more detail since it is the most valuable study in scientific research.

Clinical research starts with forming a hypothesis. A hypothesis can be defined as a claim put forward about the value of a population parameter based on sampling. There are two types of hypotheses in statistics.

• H 0 hypothesis is called a control or null hypothesis. It is the hypothesis put forward in research, which implies that there is no difference between the groups under consideration. If this hypothesis is rejected at the end of the study, it indicates that a difference exists between the two treatments under consideration.
• H 1 hypothesis is called an alternative hypothesis. It is hypothesised against a null hypothesis, which implies that a difference exists between the groups under consideration. For example, consider the following hypothesis: drug A has an analgesic effect. Control or null hypothesis (H 0 ): there is no difference between drug A and placebo with regard to the analgesic effect. The alternative hypothesis (H 1 ) is applicable if a difference exists between drug A and placebo with regard to the analgesic effect.

The planning phase comes after the determination of a hypothesis. A clinical research plan is called a protocol . In a protocol, the reasons for research, number and qualities of participants, tests to be applied, study duration and what information to be gathered from the participants should be found and conformity criteria should be developed.

The selection of participant groups to be included in the study is important. Inclusion and exclusion criteria of the study for the participants should be determined. Inclusion criteria should be defined in the form of demographic characteristics (age, gender, etc.) of the participant group and the exclusion criteria as the diseases that may influence the study, age ranges, cases involving pregnancy and lactation, continuously used drugs and participants’ cooperation.

The next stage is methodology. Methodology can be grouped under subheadings, namely, the calculation of number of subjects, blinding (masking), randomisation, selection of operation to be applied, use of placebo and criteria for stopping and changing the treatment.

I. Calculation of the Number of Subjects

The entire source from which the data are obtained is called a universe or population . A small group selected from a certain universe based on certain rules and which is accepted to highly represent the universe from which it is selected is called a sample and the characteristics of the population from which the data are collected are called variables. If data is collected from the entire population, such an instance is called a parameter . Conducting a study on the sample rather than the entire population is easier and less costly. Many factors influence the determination of the sample size. Firstly, the type of variable should be determined. Variables are classified as categorical (qualitative, non-numerical) or numerical (quantitative). Individuals in categorical variables are classified according to their characteristics. Categorical variables are indicated as nominal and ordinal (ordered). In nominal variables, the application of a category depends on the researcher’s preference. For instance, a female participant can be considered first and then the male participant, or vice versa. An ordinal (ordered) variable is ordered from small to large or vice versa (e.g. ordering obese patients based on their weights-from the lightest to the heaviest or vice versa). A categorical variable may have more than one characteristic: such variables are called binary or dichotomous (e.g. a participant may be both female and obese).

If the variable has numerical (quantitative) characteristics and these characteristics cannot be categorised, then it is called a numerical variable. Numerical variables are either discrete or continuous. For example, the number of operations with spinal anaesthesia represents a discrete variable. The haemoglobin value or height represents a continuous variable.

Statistical analyses that need to be employed depend on the type of variable. The determination of variables is necessary for selecting the statistical method as well as software in SPSS. While categorical variables are presented as numbers and percentages, numerical variables are represented using measures such as mean and standard deviation. It may be necessary to use mean in categorising some cases such as the following: even though the variable is categorical (qualitative, non-numerical) when Visual Analogue Scale (VAS) is used (since a numerical value is obtained), it is classified as a numerical variable: such variables are averaged.

Clinical research is carried out on the sample and generalised to the population. Accordingly, the number of samples should be correctly determined. Different sample size formulas are used on the basis of the statistical method to be used. When the sample size increases, error probability decreases. The sample size is calculated based on the primary hypothesis. The determination of a sample size before beginning the research specifies the power of the study. Power analysis enables the acquisition of realistic results in the research, and it is used for comparing two or more clinical research methods.

Because of the difference in the formulas used in calculating power analysis and number of samples for clinical research, it facilitates the use of computer programs for making calculations.

It is necessary to know certain parameters in order to calculate the number of samples by power analysis.

• Type-I (α) and type-II (β) error levels
• Difference between groups (d-difference) and effect size (ES)
• Distribution ratio of groups
• Direction of research hypothesis (H1)

a. Type-I (α) and Type-II (β) Error (β) Levels

Two types of errors can be made while accepting or rejecting H 0 hypothesis in a hypothesis test. Type-I error (α) level is the probability of finding a difference at the end of the research when there is no difference between the two applications. In other words, it is the rejection of the hypothesis when H 0 is actually correct and it is known as α error or p value. For instance, when the size is determined, type-I error level is accepted as 0.05 or 0.01.

Another error that can be made during a hypothesis test is a type-II error. It is the acceptance of a wrongly hypothesised H 0 hypothesis. In fact, it is the probability of failing to find a difference when there is a difference between the two applications. The power of a test is the ability of that test to find a difference that actually exists. Therefore, it is related to the type-II error level.

Since the type-II error risk is expressed as β, the power of the test is defined as 1–β. When a type-II error is 0.20, the power of the test is 0.80. Type-I (α) and type-II (β) errors can be intentional. The reason to intentionally make such an error is the necessity to look at the events from the opposite perspective.

b. Difference between Groups and ES

ES is defined as the state in which statistical difference also has clinically significance: ES≥0.5 is desirable. The difference between groups is the absolute difference between the groups compared in clinical research.

c. Allocation Ratio of Groups

The allocation ratio of groups is effective in determining the number of samples. If the number of samples is desired to be determined at the lowest level, the rate should be kept as 1/1.

d. Direction of Hypothesis (H1)

The direction of hypothesis in clinical research may be one-sided or two-sided. While one-sided hypotheses hypothesis test differences in the direction of size, two-sided hypotheses hypothesis test differences without direction. The power of the test in two-sided hypotheses is lower than one-sided hypotheses.

After these four variables are determined, they are entered in the appropriate computer program and the number of samples is calculated. Statistical packaged software programs such as Statistica, NCSS and G-Power may be used for power analysis and calculating the number of samples. When the samples size is calculated, if there is a decrease in α, difference between groups, ES and number of samples, then the standard deviation increases and power decreases. The power in two-sided hypothesis is lower. It is ethically appropriate to consider the determination of sample size, particularly in animal experiments, at the beginning of the study. The phase of the study is also important in the determination of number of subjects to be included in drug studies. Usually, phase-I studies are used to determine the safety profile of a drug or product, and they are generally conducted on a few healthy volunteers. If no unacceptable toxicity is detected during phase-I studies, phase-II studies may be carried out. Phase-II studies are proof-of-concept studies conducted on a larger number (100–500) of volunteer patients. When the effectiveness of the drug or product is evident in phase-II studies, phase-III studies can be initiated. These are randomised, double-blinded, placebo or standard treatment-controlled studies. Volunteer patients are periodically followed-up with respect to the effectiveness and side effects of the drug. It can generally last 1–4 years and is valuable during licensing and releasing the drug to the general market. Then, phase-IV studies begin in which long-term safety is investigated (indication, dose, mode of application, safety, effectiveness, etc.) on thousands of volunteer patients.

II. Blinding (Masking) and Randomisation Methods

When the methodology of clinical research is prepared, precautions should be taken to prevent taking sides. For this reason, techniques such as randomisation and blinding (masking) are used. Comparative studies are the most ideal ones in clinical research.

Blinding Method

A case in which the treatments applied to participants of clinical research should be kept unknown is called the blinding method . If the participant does not know what it receives, it is called a single-blind study; if even the researcher does not know, it is called a double-blind study. When there is a probability of knowing which drug is given in the order of application, when uninformed staff administers the drug, it is called in-house blinding. In case the study drug is known in its pharmaceutical form, a double-dummy blinding test is conducted. Intravenous drug is given to one group and a placebo tablet is given to the comparison group; then, the placebo tablet is given to the group that received the intravenous drug and intravenous drug in addition to placebo tablet is given to the comparison group. In this manner, each group receives both the intravenous and tablet forms of the drug. In case a third party interested in the study is involved and it also does not know about the drug (along with the statistician), it is called third-party blinding.

Randomisation Method

The selection of patients for the study groups should be random. Randomisation methods are used for such selection, which prevent conscious or unconscious manipulations in the selection of patients ( 8 ).

No factor pertaining to the patient should provide preference of one treatment to the other during randomisation. This characteristic is the most important difference separating randomised clinical studies from prospective and synchronous studies with experimental groups. Randomisation strengthens the study design and enables the determination of reliable scientific knowledge ( 2 ).

The easiest method is simple randomisation, e.g. determination of the type of anaesthesia to be administered to a patient by tossing a coin. In this method, when the number of samples is kept high, a balanced distribution is created. When the number of samples is low, there will be an imbalance between the groups. In this case, stratification and blocking have to be added to randomisation. Stratification is the classification of patients one or more times according to prognostic features determined by the researcher and blocking is the selection of a certain number of patients for each stratification process. The number of stratification processes should be determined at the beginning of the study.

As the number of stratification processes increases, performing the study and balancing the groups become difficult. For this reason, stratification characteristics and limitations should be effectively determined at the beginning of the study. It is not mandatory for the stratifications to have equal intervals. Despite all the precautions, an imbalance might occur between the groups before beginning the research. In such circumstances, post-stratification or restandardisation may be conducted according to the prognostic factors.

The main characteristic of applying blinding (masking) and randomisation is the prevention of bias. Therefore, it is worthwhile to comprehensively examine bias at this stage.

Bias and Chicanery

While conducting clinical research, errors can be introduced voluntarily or involuntarily at a number of stages, such as design, population selection, calculating the number of samples, non-compliance with study protocol, data entry and selection of statistical method. Bias is taking sides of individuals in line with their own decisions, views and ideological preferences ( 9 ). In order for an error to lead to bias, it has to be a systematic error. Systematic errors in controlled studies generally cause the results of one group to move in a different direction as compared to the other. It has to be understood that scientific research is generally prone to errors. However, random errors (or, in other words, ‘the luck factor’-in which bias is unintended-do not lead to bias ( 10 ).

Another issue, which is different from bias, is chicanery. It is defined as voluntarily changing the interventions, results and data of patients in an unethical manner or copying data from other studies. Comparatively, bias may not be done consciously.

In case unexpected results or outliers are found while the study is analysed, if possible, such data should be re-included into the study since the complete exclusion of data from a study endangers its reliability. In such a case, evaluation needs to be made with and without outliers. It is insignificant if no difference is found. However, if there is a difference, the results with outliers are re-evaluated. If there is no error, then the outlier is included in the study (as the outlier may be a result). It should be noted that re-evaluation of data in anaesthesiology is not possible.

Statistical evaluation methods should be determined at the design stage so as not to encounter unexpected results in clinical research. The data should be evaluated before the end of the study and without entering into details in research that are time-consuming and involve several samples. This is called an interim analysis . The date of interim analysis should be determined at the beginning of the study. The purpose of making interim analysis is to prevent unnecessary cost and effort since it may be necessary to conclude the research after the interim analysis, e.g. studies in which there is no possibility to validate the hypothesis at the end or the occurrence of different side effects of the drug to be used. The accuracy of the hypothesis and number of samples are compared. Statistical significance levels in interim analysis are very important. If the data level is significant, the hypothesis is validated even if the result turns out to be insignificant after the date of the analysis.

Another important point to be considered is the necessity to conclude the participants’ treatment within the period specified in the study protocol. When the result of the study is achieved earlier and unexpected situations develop, the treatment is concluded earlier. Moreover, the participant may quit the study at its own behest, may die or unpredictable situations (e.g. pregnancy) may develop. The participant can also quit the study whenever it wants, even if the study has not ended ( 7 ).

In case the results of a study are contrary to already known or expected results, the expected quality level of the study suggesting the contradiction may be higher than the studies supporting what is known in that subject. This type of bias is called confirmation bias. The presence of well-known mechanisms and logical inference from them may create problems in the evaluation of data. This is called plausibility bias.

Another type of bias is expectation bias. If a result different from the known results has been achieved and it is against the editor’s will, it can be challenged. Bias may be introduced during the publication of studies, such as publishing only positive results, selection of study results in a way to support a view or prevention of their publication. Some editors may only publish research that extols only the positive results or results that they desire.

Bias may be introduced for advertisement or economic reasons. Economic pressure may be applied on the editor, particularly in the cases of studies involving drugs and new medical devices. This is called commercial bias.

In recent years, before beginning a study, it has been recommended to record it on the Web site www.clinicaltrials.gov for the purpose of facilitating systematic interpretation and analysis in scientific research, informing other researchers, preventing bias, provision of writing in a standard format, enhancing contribution of research results to the general literature and enabling early intervention of an institution for support. This Web site is a service of the US National Institutes of Health.

The last stage in the methodology of clinical studies is the selection of intervention to be conducted. Placebo use assumes an important place in interventions. In Latin, placebo means ‘I will be fine’. In medical literature, it refers to substances that are not curative, do not have active ingredients and have various pharmaceutical forms. Although placebos do not have active drug characteristic, they have shown effective analgesic characteristics, particularly in algology applications; further, its use prevents bias in comparative studies. If a placebo has a positive impact on a participant, it is called the placebo effect ; on the contrary, if it has a negative impact, it is called the nocebo effect . Another type of therapy that can be used in clinical research is sham application. Although a researcher does not cure the patient, the researcher may compare those who receive therapy and undergo sham. It has been seen that sham therapies also exhibit a placebo effect. In particular, sham therapies are used in acupuncture applications ( 11 ). While placebo is a substance, sham is a type of clinical application.

Ethically, the patient has to receive appropriate therapy. For this reason, if its use prevents effective treatment, it causes great problem with regard to patient health and legalities.

Before medical research is conducted with human subjects, predictable risks, drawbacks and benefits must be evaluated for individuals or groups participating in the study. Precautions must be taken for reducing the risk to a minimum level. The risks during the study should be followed, evaluated and recorded by the researcher ( 1 ).

After the methodology for a clinical study is determined, dealing with the ‘Ethics Committee’ forms the next stage. The purpose of the ethics committee is to protect the rights, safety and well-being of volunteers taking part in the clinical research, considering the scientific method and concerns of society. The ethics committee examines the studies presented in time, comprehensively and independently, with regard to ethics and science; in line with the Declaration of Helsinki and following national and international standards concerning ‘Good Clinical Practice’. The method to be followed in the formation of the ethics committee should be developed without any kind of prejudice and to examine the applications with regard to ethics and science within the framework of the ethics committee, Regulation on Clinical Trials and Good Clinical Practice ( www.iku.com ). The necessary documents to be presented to the ethics committee are research protocol, volunteer consent form, budget contract, Declaration of Helsinki, curriculum vitae of researchers, similar or explanatory literature samples, supporting institution approval certificate and patient follow-up form.

Only one sister/brother, mother, father, son/daughter and wife/husband can take charge in the same ethics committee. A rector, vice rector, dean, deputy dean, provincial healthcare director and chief physician cannot be members of the ethics committee.

Members of the ethics committee can work as researchers or coordinators in clinical research. However, during research meetings in which members of the ethics committee are researchers or coordinators, they must leave the session and they cannot sign-off on decisions. If the number of members in the ethics committee for a particular research is so high that it is impossible to take a decision, the clinical research is presented to another ethics committee in the same province. If there is no ethics committee in the same province, an ethics committee in the closest settlement is found.

Thereafter, researchers need to inform the participants using an informed consent form. This form should explain the content of clinical study, potential benefits of the study, alternatives and risks (if any). It should be easy, comprehensible, conforming to spelling rules and written in plain language understandable by the participant.

This form assists the participants in taking a decision regarding participation in the study. It should aim to protect the participants. The participant should be included in the study only after it signs the informed consent form; the participant can quit the study whenever required, even when the study has not ended ( 7 ).

Peer-review: Externally peer-reviewed.

Author Contributions: Concept - C.Ö.Ç., A.D.; Design - C.Ö.Ç.; Supervision - A.D.; Resource - C.Ö.Ç., A.D.; Materials - C.Ö.Ç., A.D.; Analysis and/or Interpretation - C.Ö.Ç., A.D.; Literature Search - C.Ö.Ç.; Writing Manuscript - C.Ö.Ç.; Critical Review - A.D.; Other - C.Ö.Ç., A.D.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study has received no financial support.

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Science, health, and public trust.

September 8, 2021

Explaining How Research Works

We’ve heard “follow the science” a lot during the pandemic. But it seems science has taken us on a long and winding road filled with twists and turns, even changing directions at times. That’s led some people to feel they can’t trust science. But when what we know changes, it often means science is working.

Explaining the scientific process may be one way that science communicators can help maintain public trust in science. Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle.

Questions about how the world works are often investigated on many different levels. For example, scientists can look at the different atoms in a molecule, cells in a tissue, or how different tissues or systems affect each other. Researchers often must choose one or a finite number of ways to investigate a question. It can take many different studies using different approaches to start piecing the whole picture together.

Sometimes it might seem like research results contradict each other. But often, studies are just looking at different aspects of the same problem. Researchers can also investigate a question using different techniques or timeframes. That may lead them to arrive at different conclusions from the same data.

Using the data available at the time of their study, scientists develop different explanations, or models. New information may mean that a novel model needs to be developed to account for it. The models that prevail are those that can withstand the test of time and incorporate new information. Science is a constantly evolving and self-correcting process.

Scientists gain more confidence about a model through the scientific process. They replicate each other’s work. They present at conferences. And papers undergo peer review, in which experts in the field review the work before it can be published in scientific journals. This helps ensure that the study is up to current scientific standards and maintains a level of integrity. Peer reviewers may find problems with the experiments or think different experiments are needed to justify the conclusions. They might even offer new ways to interpret the data.

It’s important for science communicators to consider which stage a study is at in the scientific process when deciding whether to cover it. Some studies are posted on preprint servers for other scientists to start weighing in on and haven’t yet been fully vetted. Results that haven't yet been subjected to scientific scrutiny should be reported on with care and context to avoid confusion or frustration from readers.

We’ve developed a one-page guide, "How Research Works: Understanding the Process of Science" to help communicators put the process of science into perspective. We hope it can serve as a useful resource to help explain why science changes—and why it’s important to expect that change. Please take a look and share your thoughts with us by sending an email to  [email protected].

Below are some additional resources:

• Discoveries in Basic Science: A Perfectly Imperfect Process
• When Clinical Research Is in the News
• What is Basic Science and Why is it Important?
• ​ What is a Research Organism?
• What Are Clinical Trials and Studies?
• Basic Research – Digital Media Kit
• Decoding Science: How Does Science Know What It Knows? (NAS)
• Can Science Help People Make Decisions ? (NAS)

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

Cite this chapter

You have full access to this open access chapter

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

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Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

You have full access to this open access chapter,  Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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How research works: understanding the process of science.

Have you ever wondered how research works? How scientists make discoveries about our health and the world around us? Whether they’re studying plants, animals, humans, or something else in our world, they follow the scientific method. But this method isn’t always—or even usually—a straight line, and often the answers are unexpected and lead to more questions. Let’s dive in to see how it all works.

The Question Scientists start with a question about something they observe in the world. They develop a hypothesis, which is a testable prediction of what the answer to their question will be. Often their predictions turn out to be correct, but sometimes searching for the answer leads to unexpected outcomes.

The Techniques To test their hypotheses, scientists conduct experiments. They use many different tools and techniques, and sometimes they need to invent a new tool to fully answer their question. They may also work with one or more scientists with different areas of expertise to approach the question from other angles and get a more complete answer to their question.

The Evidence Throughout their experiments, scientists collect and analyze their data. They reach conclusions based on those analyses and determine whether their results match the predictions from their hypothesis. Often these conclusions trigger new questions and new hypotheses to test.

Researchers share their findings with one another by publishing papers in scientific journals and giving presentations at meetings. Data sharing is very important for the scientific field, and although some results may seem insignificant, each finding is often a small piece of a larger puzzle. That small piece may spark a new question and ultimately lead to new findings.

Sometimes research results seem to contradict each other, but this doesn’t necessarily mean that the results are wrong. Instead, it often means that the researchers used different tools, methods, or timeframes to obtain their results. The results of a single study are usually unable to fully explain the complex systems in the world around us. We must consider how results from many research studies fit together. This perspective gives us a more complete picture of what’s really happening.

Even if the scientific process doesn’t answer the original question, the knowledge gained may help provide other answers that lead to new hypotheses and discoveries.

Learn more about the importance of communicating how this process works in the NIH News in Health article, “ Explaining How Research Works .”

This post is a great supplement to Pathways: The Basic Science Careers Issue.

Pathways introduces the important role that scientists play in understanding the world around us, and all scientists use the scientific method as they make discoveries—which is explained in this post.

Learn more in our Educator’s Corner .

2 Replies to “How Research Works: Understanding the Process of Science”

Nice basic explanation. I believe informing the lay public on how science works, how parts of the body interact, etc. is a worthwhile endeavor. You all Rock! Now, we need to spread the word ‼️❗️‼️ Maybe eith a unique app. And one day, with VR and incentives to read & answer a couple questions.

As you know, the importance of an informed population is what will keep democracy alive. Plus it will improve peoples overall wellness & life outcomes.

Thanks for this clear explanation for the person who does not know science. Without getting too technical or advanced, it might be helpful to follow your explanation of replication with a reference to meta-analysis. You might say something as simple as, “Meta-analysis is a method for doing research on all the best research; meta-analytic research confirms the overall trend in results, even when the best studies show different results.”

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July 22, 2014

Why Science Is Important

Our own track record proves that steady federal funding support leads to success

By Mariette DiChristina

As editor in chief and senior vice president, I’ve given talks to a range of audiences about why science is important to humanity’s future wellbeing. But Thursday, July 17, was not the typical discussion: I was privileged to join three science experts as witnesses at the U.S. Senate Committee on Commerce, Science, and Transportation hearing, “The Federal Research Portfolio: Capitalizing on Investments in R&D.” The hearing considered the federal government’s role in research and development (R&D), and the nation’s STEM education and outreach initiatives.

Others in the Capitol hearing room were Vinton G. Cerf , computer scientist, Google’s Internet Evangelist and one of the fathers of the Internet; Neal F. Lane , former director of the White House Office of Science and Technology Policy; and Stephen E. Fienberg , professor of statistics and social science at Carnegie Mellon University.

Recognizing the need for long-term investments in science and technology, Congress passed the America COMPETES Acts of 2007 and 2010 to significantly increase federal R&D budgets, to promote STEM (science, technology, engineering and mathematics) education and to support the innovation necessary for economic growth.

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Each witness had five minutes to make verbal remarks in addition to the written testimony. Below is a text of mine. This In-Depth Report includes all of the full-length testimonies, a video of the hearing and other articles about funding and basic research.  

Thank you, Chairman Rockefeller, Ranking Member Thune, and the Committee, for the privilege of addressing you today.

My name is Mariette DiChristina, and I’m the editor in chief of Scientific American , which has chronicled the power of U.S. basic research since its founding in 1845.

Scientific American started the first branch of the U.S. patent agency in 1850, and Thomas Edison is among the researchers who’ve visited our offices. Albert Einstein wrote for Scientific American , as have more than 150 Nobel laureates and many winners of the U.S. National Medals of Science and Technology. It reaches more than 3.5 million in print and more than 6 million each month online, including leaders in business and policy, educators, students and science enthusiasts.

Science is the engine of prosperity. Economists have said that a third to a half of U.S. economic growth has resulted from basic research since World War II. The cars and trains that got us here today, our smart phones, the energy that lights this chamber, the clothes we wear, the food we eat: All of these were developed and improved through research.

But before these applications existed, researchers had to study the basic concepts that provided a sound foundation—and they did those studies not necessarily knowing where they would lead. I know Einstein wasn’t thinking about GPS in smart phones when he formulated his theory of relativity a hundred years ago. But knowing how spacetime works helps make our measurements from orbiting GPS satellites accurate.

And Elizabeth Blackburn was just curious about what’s at the end of chromosomes when she started studying the DNA of pond scum in the 1970s. The NIH started funding her work in 1978. In 2009, she and fellow NIH grantees, Carol Greider and Jack Szostak, won a Nobel for their work in understanding what’s at the end of those chromosomes—structures called telomeres, which we now know play an important role in human cancers and diseases of aging.

Examples like Elizabeth Blackburn show why providing steady and sufficient support for basic research should be a national priority. We need to take the long view on R&D for the nation’s future, just as we need to nurture our children over their entire K-12 careers, so they can succeed in an increasingly competitive global marketplace.

Research takes time. Typical funding grants average five years long. It takes time to run the experiments, gather the data, analyze it properly, and confirm the findings.

And our own track record proves that steady federal funding support leads to success. U.S. federal funding was key to nearly 90 percent of almost 100 top innovations from 1971 to 2006 identified by R&D Magazine.

Our nation’s ability to handle today’s pressing issues, from providing energy security to curing illnesses to living sustainably in a finite world, will require the innovations that arise from basic research.

It also provides a good return. In a particularly strong example, the Human Genome Project paid back $141 in jobs and growth for each dollar invested. In general, the return for publicly funded R&D is between 30 and 100 percent.

Basic research can be inspiring. The Zooniverse Web site, for instance, lets anybody catalog heavenly objects from NASA images. It has more than a million volunteer citizen scientists! Thousands of Scientific American ’s own volunteers catalogued more than 100,000 whale calls in 2 months—equal to 2 years of lab work. The Maker movement is such a phenomenon that the U.S. Office of Science & Technology Policy is holding Maker Faire events.

Unfortunately, since the 1980s, R&D spending overall has flattened and even declined in real dollars, according to a report from the Congressional Budget Office on R&D and Productivity Growth. Because of the length of time needed for research, also, the sequester cuts will affect progress for years to come in forestalled and canceled work, and will disproportionately affect and discourage our younger researchers.

Meanwhile, countries such as China are nipping at our heels. Earlier this year, China’s rate of GDP investment surpassed that of the 28 member states of the European Union, and could exceed that of the U.S. itself in a little over half a decade, according to the 2014 Global R&D Forecast by Battelle and R&D Magazine. Japan, Denmark, Finland, Germany, Israel and Sweden already spend a greater percentage of their GDP on research than the U.S., according to World Bank.

A strong STEM education pipeline is also critical. Over the past 10 years, STEM jobs grew 3x as fast as non-STEM, says the U.S. Department of Commerce, and our leading technology companies are often challenged to fill the necessary openings.

For one more view, I turned to a member of the next generation. I told my older daughter, Selina, who plans to double major in computer science and graphic design, that I'd be speaking about this topic. I asked her what she would say about science.

“That’s easy, mom,” she said to me. “It’s the foundation of everything.”

And so it is. Science is a system for exploring, and for innovation. It can fuel our nation’s economic growth. It can form a path for our young people in a competitive global marketplace. And it can fire our imagination.

That’s why basic-science research deserves our steady commitment and investment. Thank you for your kind attention.

Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe. 

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

The steps of the scientific method go something like this, according to Highline College :

• Make an observation or observations.
• Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
• Test the hypothesis and predictions in an experiment that can be reproduced.
• Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
• Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

• The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
• Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
• An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
• An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed. 

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

• This video from City University of New York delves into the basics of what defines science.
• Learn about what makes science science in this book excerpt from Washington State University .
• This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. ​​ https://undsci.berkeley.edu/article/0_0_0/intro_01  

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm  

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html  

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045  

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group  

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml  

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/  

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "​Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone  

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What  

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

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What is science—and why does it matter?

by Chris Woodford . Last updated: January 4, 2022.

Q uestions, questions. Why this...? Why that...? How does this...? How come that...? If you're the sort of person who's always seeking answers, maybe you're a scientist of sorts without knowing it? Knowing , in fact, is what science is all about: the term "science" is linked to Latin words like scire ("to know") and scientia ("knowledge"), so it's the process of finding answers to how and why the world works as it does. From why the sky's blue to how your nose smells, from why boats float on water to what makes us happy or sad , you can seek answers—and enlightenment— in all kinds of ways: you can ask your friends their opinion, pray to a god, paint pictures, write songs, or meditate on a mountain, scratching your head. What makes science so different from these other ways of thinking about things—and why does it matter?

What is science?

What makes science different is that it's a very systematic way of building up knowledge. It uses logical thinking to explain why things work or how things happen based on evidence gathered through observation and experiment. Slowly and surely, science comes up with coherent explanations called theories that mesh with bigger theories to make ever more comprehensive accounts of what's going on around us. So, for example, Isaac Newton's comprehensive, "universal" theory of gravity was built on smaller theories like Galileo's observations of how falling objects hurtle toward Earth and Johannes Kepler's ideas about the planets sweeping through space, themselves based on earlier science dating back to ancient times. Newton's ideas, in turn, became part of a wider explanation of gravity, known as the general theory of relativity, which Albert Einstein put forward in the early 20th century. Science is a jigsaw puzzle, the theories are the pieces, and as different theories gradually lock together, they give us an ever-expanding picture of how our world works.

The scientific method

“ The important thing is not to stop questioning. ” Albert Einstein

Why's the sky blue? If you don't know the actual explanation, you could probably guess at all sorts of answers—and so could everyone else. If we just asked people what they thought, we could easily end up with 50 or 500 possible accounts. How do we figure out which of these is the right one?

Scientists use an approach called the scientific method . First, they observe or measure something (the sky being blue, for example) very carefully and systematically, which is known as gathering data. (When is it blue? Precisely what shade of blue? Is it ever other colors? When?) From this, they come up with a tentative, logical explanation known as a hypothesis . (It could be something like: the sky is blue because there's water in the air.) The hypothesis should suggest ways in which it can be tested, which are known as experiments . (Is the sky blue on cloudy days, when there's apparently more water in the sky, or dry days, when it's sunnier?) By carrying out experiments, a scientist can test a hypothesis to see if it's a good explanation that accounts for all the evidence.

Photo: Which of Earth's many lifeforms can survive on other planets or in space stations? It's something we need to test with experiments like this one, which looks at how different genes are turned "on" or "off" in space. Photo courtesy of NASA .

Although experiments can be quick and simple, they can also be intricate and complex. Most experiments compare a situation where we've deliberately changed something (say, doing more exercise to see if we feel better) with another situation where we haven't. That's called a controlled experiment and it allows us to see whether the thing we change makes any difference. (We can do other experiments that change other things, one at a time, and see what difference that makes instead.) Experiments that come up with mathematical results also have to prove that those results couldn't have happened purely by chance. There are ways of testing experimental data using math and if the data is better than a chance result, we say it's statistically significant .

If a hypothesis can't be tested by experiment, it's usually rejected as bad science from the start. So if your idea of why the sky is blue is that Martians got out their paint pots when you weren't looking, that's essentially untestable: there's no evidence and no obvious way of getting any, so the hypothesis is a non-starter. That doesn't mean a hypothesis has to be tested immediately: sometimes it takes quite a while to devise just the right experiment. Albert Einstein first put forward his general theory of relativity in 1915. But he had to wait four years before another physicist, Sir Arthur Eddington, was able to confirm it , with the help of a famous solar eclipse.

“ Science is a method to keep yourself from kidding yourself. ” Edwin Land

Why is evidence so important to science? Medicine is probably the best example. If you're sick, you want an effective treatment that makes you better; if you're dying, you want a cure. It's perfectly possible that quack cures will sometimes help people get better, either through pure chance or the very intriguing (and very real) placebo effect . But to come up with medical treatments that consistently improve people's lives, we need to carry out experiments and build up evidence that those treatments really do work, consistently, and in all the different groups of people who might try them; we also need to be sure they don't do more harm than good. Science stops us falling into the trap of gullibility—of believing specious ideas (things that sound right that are actually wrong). As Edwin Land, the physicist inventor of the Polaroid camera once said: "Science is a method to keep yourself from kidding yourself."

What is a theory?

If there's good evidence, a tentative and very fluid hypothesis starts to solidify into a more formal, generally accepted explanation of something, which is called a theory . In other words, a theory is a hypothesis confirmed by experimental evidence or other observations. The more and better the evidence, the stronger the theory—and the more things a theory can explain, the better it is. Importantly, evidence for a theory has to come from more than one person or group: in other words, the results of one team's work has to be replicated (repeated) by others. Theories also have to be published and discussed by the wider scientific community (usually in reputable scientific journals) in a process known as peer review , which gives other people the opportunity to spot flaws in your theory or the methods you used to test it. If any evidence contradicts a theory, the theory is either wrong or incomplete, which means a better theory is needed. Sometimes wrong theories come from bad experiments that supply incorrect data or other kinds of misleading evidence. It's important to try to disprove theories ("If we see this happening, the theory must be wrong") and not just confirm them ("If we see this happening, it agrees with our theory"), though it's a sign of a good theory if it can be properly defended against criticism.

Artwork: The Periodic Table is part of a brilliant theory that explains why different chemical elements have similar properties.

The best theories—things like the theory of evolution —have "evolved" (if you'll excuse the pun) over decades or centuries, supported by many different kinds of evidence involving thousands of experiments and studies by many different scientists from all sorts of fields. It can take a long time for an excellent theory like this to be accepted. In much the same way, wrong-headed theories will sometimes take a long time to disappear. For example, it was originally believed that Earth was the center of the universe and the Sun and planets revolved around it. Known as the geocentric theory (literally, "Earth-centered" theory), that was widely accepted in ancient times, but evidence slowly emerged that it was wrong. To get around this, early scientists could simply have thrown that theory away and come up with a totally new one. Instead, what they did was come up with increasingly tortuous fudges to account for the discrepancies. Eventually, scientists like Kepler, Galileo, and Copernicus developed a rival heliocentric theory , in which the Sun sits at the center of things, which is what people believe today. Another commonly believed explanation that lasted a very long time was the miasma theory —the idea that diseases were passed on by bad air. It persisted as a plausible explanation of disease from ancient times right up until the late 19th century, when growing evidence led to a much better explanation known as the germ theory (the idea that bacteria and viruses cause diseases).

Photo: Albert Einstein's theory of relativity wasn't just his throwaway "opinion": it was a explanation designed to account for all the facts Einstein knew about things like light, gravity, and motion. Photo courtesy of US Library of Congress .

It's important to realize that calling something "a theory" doesn't mean it's flaky, speculative, or just an opinion. The theory of evolution is supported by a huge mass of very different evidence and, though there are still gaps in our understanding of how it works, it's generally accepted as the best explanation of how the modern pattern of humans and other living creatures came to arrive on Earth. In other words, it's the best explanation for all the facts that we have. Einstein's original, "special" theory of relativity was also supported by evidence, but there were various things it couldn't explain. That was why Einstein soon developed a deeper, more comprehensive explanation in the shape of his "general" theory of relativity. This, too, has gaps and is by no means a perfect theory (for example, it's an ongoing challenge to reconcile Einstein's ideas with quantum theory, the currently favored explanation of how the atomic world works). Crucially, no scientific theory can ever be proved completely correct: someone could always come up with new evidence tomorrow that disproves it. But that doesn't mean every theory is automatically suspect. If a theory has been around a long time and it's supported by a huge body of different evidence (like the theory of evolution), we can be reasonably confident that it's right. Even so, as the heliocentric theory shows, we can never be complacent: as scientists, our minds should always be open. The key point is that science is a work in progress; it's like a vast jigsaw puzzle that will never be complete.

“ Some claim that evolution is just a theory, as if it were merely an opinion. The theory of evolution—like the theory of gravity—is a scientific fact. ” Richard Dawkins

Types of science

If science is a method —a way of building knowledge about the world—that suggests it's a kind of tool we can apply to all kinds of things. From physics and chemistry to medicine and sociology, scientific methods have been used to study every aspect of our world. Different sciences are very different from one another and range from the highly abstract, mathematical ideas of theoretical physics to the very concrete ideas of medical science, which are firmly grounded in biological observations of how our bodies work.

Photo: Much of space science is applied physics—ordinary physics theories applied to the problems of space travel or living in microgravity. Here, three of NASA's women scientists are practicing weightlessness in a flotation tank at Marshall Space Flight Center. Photo courtesy of NASA .

Science and its rivals

The scientific method—and the fundamental importance of evidence—is the big difference between science and other ways of thinking about our place in the world, including myths, superstitions, art, religion, and things like astrology. You might be a superstitious kind of person who doesn't walk on the cracks in the pavement, but there's no evidence that walking on cracks is either bad or good for you in any way—and no obvious mechanism by which it ever might be. Myths and superstitions may be fascinating and fun, but they're not credible explanations that can compete with science.

Photo: Science tells us plants are green because of the chloroplasts inside them, which capture the Sun's energy a bit like miniature solar cells . Can religion, art, or myth explain things like this? Osiris, the ancient Egyptian god of agriculture and fertility, had green skin, hinting at a connection with vegetation, but that's hardly an explanation! Photograph courtesy of NASA .

Science versus religion?

What about religion? It's perfectly fine to have religious beliefs about why we see colors in the sky or to paint a picture that shows a rainbow forming, but art and religion are a world away from scientific explanations. They might even be based on meticulous observations, but they still lack the logical rigor of scientific theories. You might say "Well, a religious miracle is evidence for [such and such]," but that's hardly a scientific explanation. Miracles aren't testable, they're not repeatable, and they generally have other, more scientific explanations behind them. That's not to say that religion has no value; the value it has as a coherent belief system, which helps people to live morally good, spiritually enriched, happy and fulfilled lives, is very different from the value of science. You can pray, if you have lung cancer, and it could help you in all kinds of ways—but medical treatments, based on years of evidence-based research, are much more likely to cure you.

Science and art

“ To develop a complete mind: Study the science of art; Study the art of science. Learn how to see. Realize that everything connects to everything else. ” Leonardo da Vinci

When people are studying in schools and colleges, they often think of themselves as "arty" or "sciencey," as though there's a sharp line between the two. Arts subjects are meant to be more human, creative, poetic, emotional, and romantic; sciences are considered more logical, rational, methodical, prosaic, and perhaps even a bit plodding and boring. Of course, that's all a matter of opinion: it's hard to think of anything more human than medicine, for example, which is quintessentially scientific. It's never really clear why people want to build high walls between the arts and sciences. A genius like Leonardo da Vinci obviously straddled the divide; modern artists and scientists also work on similar or overlapping problems. You could argue, for example, that, with their pursuit of cubism, artists like Cezanne, Braque, and Picasso were studying very similar problems to scientists like Einstein. Bridget Riley's op-art clearly has much in common with a branch of psychology called psychophysics (which studies how the eyes and brain perceive light, colors, and patterns). Artist Josef Albers was just as much a scientist of color as Isaac Newton or Thomas Young. Less obviously, a sculptor like Rodin was arguably just as preoccupied with gravity (in his own way) as a scientist like Galileo or Newton.

The very short story of science

How did humans come up with the idea of science? What was wrong with myths, superstitions... and all those earlier, older, and often more magically enchanting ways of explaining? Science, ultimately, turned out to be a more successful intellectual engine for powering civilization. It had better answers and more useful explanations; it soon pulled ahead of the pack. It's easy to see why with an example. In hindsight, it's clear how a growing scientific understanding of electricity and magnetism in the 18th and 19th centuries enabled the development of a superb new way of harnessing, storing, and using energy that's been revolutionizing our world ever science. By contrast, it's hard to see how mystical, mythical, religious, or superstitious ways of explaining things like static electricity , lightning, or sparks could ever have spawned such fabulously useful technologies as electric cars or computers . They might be very comforting to people, as self-contained explanations of a kind, but they offer no real value going forward.

Before science

Early civilizations had systematic knowledge—astronomy and math were their strongest suits—but they didn't have what we now regard as science. People certainly made discoveries—fire, for example—and they came up with world-beating inventions like the wheel and axle . They could see those things were effective, but they didn't understand how or why (how a fire burst to life or exactly why a wheel made it easier to push a cart). Nor did they appreciate how one discovery could couple with another to make a third that was even more useful (how a fire could be used to drive a wheel—which was the thinking behind steam engines ). Early people knew how to extract metals like gold and silver from the Earth and how to refine them, but they didn't understand the relationship between different elements or the chemistry of how they combine, which is why they got sidetracked by absurd ideas like alchemy. Knowledge, such as it existed, tended to be practical rather than theoretical and very much more fragmented.

Ancient science

Photo: Thales: the ancient Greek father of modern electrical science. Credit: Photographs in the Carol M. Highsmith Archive, courtesy of Library of Congress , Prints and Photographs Division.

Science was really born in ancient times, with the Sumerians, Egyptians, and Greeks like Thales, Pythagoras, Anaximander, Aristotle, Archimedes, and Eratosthenes. Infatuated with logical reasoning and mathematics, they had both qualitative ("wordy") and quantitative ("numbery") explanations for things. The scientific foundations of physics, botany, zoology, anatomy, physiology, engineering, and medicine were all laid down in ancient times. The Romans who followed the Greeks were, by contrast, more practical and applied scientists, making huge leaps in architecture and engineering.

Dark and Golden science

“ Arabic science throughout its golden age was inextricably linked to religion; indeed, it was driven by the need of early scholars to interpret the Qur'an. ” Jim Al-Khalili

Following the collapse of the Roman Empire, scientific progress stalled in the west, in a time known as the Dark Ages, while the baton of progress passed to the Islamic world in a glorious period of science history now known as the Islamic Golden Age . Al-Khwarizmi (who gave his name to algorithms) developed algebra, Avicenna advanced medicine, Alhazen pioneered modern optics, and Al-Jazari developed ingenious machines. In the Arabic world, the best ideas from Egypt, Greece, China, India, and elsewhere fused and burned like the fuel in a modern-day rocket, before drifting back to Europe at the end of the Middle Ages. Science, in the Golden Age, helped to illuminate religion. And from then on, religious and philosophical ideas slowly started to merge with scientific ones thanks to the enlightened open minds of scholars like Peter Abelard, Thomas Aquinas, Hildegard of Bingen, and Roger Bacon.

The science revolution

True science probably began at the point where the world's best thinkers started to toss aside ancient ideas. Leonardo da Vinci blurred the boundaries between art and science, as never before or science. Another defining figure was Nicolaus Copernicus, who, as we've already seen, challenged the long-held (and religiously defended) idea that God's Earth anchored a "geocentric" Universe. Meanwhile, Belgian Andreas Vesalius published a detailed anatomical textbook superseding the ancient, out-of-date medical ideas of Galen and Avicenna. And Francis Bacon helped to formalize the scientific method.

Copernicus paved the way for Kepler and Galileo, who, in turn, opened the door for Isaac Newton and his insightful theories of gravity , motion , light , and a superb mathematical tool known as calculus (developed in parallel by German polymath Gottfried Leibniz). Meanwhile, Robert Hooke studied plants, animals, and living cells under the microscope , while William Harvey built on Vesalius's work with a pioneering theory of how blood circulates around our bodies and hugely influential ideas about magnetism. Another Robert, Robert Boyle, kick-started the systematic, experimental study of chemistry.

Artwork: Galileo Galilei—student of motion and gravity, and pioneer of telescopes. Photo courtesy of US Library of Congress .

Modern science

In physics, thanks to a steady stream of pioneers from Benjamin Franklin to Michael Faraday, the 18th and 19th centuries were the age of electricity and energy, a fusion of practical and applied ideas, science spawning technology. Over in chemistry, magical ideas like alchemy (which even Newton had toyed with) gave way to more realistic, systematic explanations based on a gradual understanding of the chemical elements as fundamental building blocks of our world. Two key figures here were Frenchman Antoine Laurent Lavoisier, who figured out the logic of how elements fused together in reactions, and Englishman John Dalton, who sketched out the beginnings of our modern atomic theory (the idea that everything is ultimately made of atoms). Their ideas would help Dmitri Mendeleev to figure out how elements related to one another in a theoretical diagram he drew up known as the Periodic Table. Meanwhile in biology, a Swedish botanist named Carl Linnaeus studied the similarities and differences between plants and animals and worked out a neat, hierarchical system of classifying species that we still use to this day. A little later, Gregor Mendel pioneered genetics (the idea that plants and animals inherit important characteristics from their parents). The work of Linnaeus and Mendel held the door wide for Charles Darwin and his life-explaining theory of evolution by "natural selection."

These seeds of modern biology spawned amazing new advances in the 20th century, most notably with Francis Crick and James Watson's discovery of the structure of DNA in 1953, and Frederick Sanger's pioneering work on DNA sequencing. But the 20th century saw many other huge advances, from Einstein's world-bending theory of relativity to Edwin Hubble's idea of the ever-expanding universe. The biggest, most revolutionary advances arguably came with a much deeper understanding of the atomic theory, with discovery piled upon discovery by such brilliant physicists as Ernest Rutherford, Niels Bohr, Lise Meitner, Enrico Fermi, Richard Feynman, and many others. Practical spin-offs of this work included everything from nuclear power plants to superconductors and supercomputers .

The power of science

Photo: Not all famous scientists are "dead white guys." African-American scientist George Washington Carver (1864?–1943) was a pioneer of 20th-century biotechnology. Born to parents who were slaves in Missouri, he discovered that he loved learning and worked hard to educate himself. Photo courtesy of US Library of Congress .

And this is how the story of science moves forward. Each theory builds on older theories, adjusts them, improves them, or kicks them entirely aside. Theories interlock with other theories, making bigger, better, and more comprehensive explanations. We learn more and more about the world and our place in it, how to solve pressing problems, how to do things better, quicker, or in less environmentally destructive ways. Time moves on, the world moves with it. But thanks to the power of science, humans always move forward , to a better place.

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• Great experiments in physics
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On other sites

• STEM Learning : A great collection of STEM resources, mostly geared to teachers and community groups.
• Youth Science Centre : Inspiring science students in the United States for several decades!
• Is It A Theory? Is It A Law? No, It's A Fact. by Richard Dawkins. RichardDawkins.net, November 30, 2015. Does calling something a "theory" mean it's no better than someone's opinion?

For older readers

• The Science Book: Big Ideas Simply Explained by DK, 2014. A concise, chronological account of the great science theories. Good for dipping in and out of, but a little short on detail.
• Scientists Who Changed History by DK, 2019. A colorful and entertaining collection of short biographies. (I was the consultant editor on this book.)
• Bad Science by Ben Goldacre. Farrar, Straus and Giroux, 2010. An entertaining account of how science should be used and how it can be misused.
• The Greatest Show on Earth: The Evidence for Evolution by Richard Dawkins, Simon and Schuster, 2009. A book about evidence-based science, using evolution as its example.
• Great Experiments in Physics edited by Morris Shamos. Dover, 1987. A great compilation of some of the best physics experiments of all time, as described by the scientists who devised them.

For younger readers

• 100 Scientists Who Made History by Andrea Mills, DK, 2018. Described as "ages 9–12," though I'd put it in the lower part of that range.
• Born Curious: 20 Girls Who Grew Up to Be Awesome Scientists by Martha Freeman, Simon and Schuster, 2020. Ages 7–12. An inspiring book for any would-be female scientists in your family.

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What is Research?

Research is the pursuit of new knowledge through the process of discovery. Scientific research involves diligent inquiry and systematic observation of phenomena. Most scientific research projects involve experimentation, often requiring testing the effect of changing conditions on the results. The conditions under which specific observations are made must be carefully controlled, and records must be meticulously maintained. This ensures that observations and results can be are reproduced. Scientific research can be basic (fundamental) or applied. What is the difference? The National Science Foundation uses the following definitions in its resource surveys:

Basic research:

The objective of basic research is to gain more comprehensive knowledge or understanding of the subject under study, without specific applications in mind. In industry, basic research is defined as research that advances scientific knowledge but does not have specific immediate commercial objectives, although it may be in fields of present or potential commercial interest.

Applied research:

Applied research is aimed at gaining knowledge or understanding to determine the means by which a specific, recognized need may be met. In industry, applied research includes investigations oriented to discovering new scientific knowledge that has specific commercial objectives with respect to products, processes, or services.

What is research at the undergraduate level?

At the undergraduate level, research is self-directed work under the guidance and supervision of a mentor/advisor ― usually a university professor. A gradual transition towards independence is encouraged as a student gains confidence and is able to work with minor supervision. Students normally participate in an ongoing research project and investigate phenomena of interest to them and their advisor.

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15 Reasons Why Science Is Important

Most science isn’t glamorous or performed in the public eye. Not everyone can be Benjamin Franklin flying a kite during a lightning storm or Marie Curie isolating radium. Most scientists never become household names, but they engage in work that can save lives, solve problems, and move the human race forward. Science affects everyone whether we know it or not. Here are 15 reasons why science is important:

#1. Science teaches you how to think analytically

Good science isn’t just about facts and figures. It teaches you how to think. When you study science, you learn how to organize and analyze huge amounts of data. You learn how to determine what’s good evidence, what’s bad, and what needs to be studied more. This type of analytical thinking is important in many other fields.

#2. Science teaches you how to solve problems

When you’re facing a problem, you can use science to help you solve it. Emergency medicine physician Gurpreet Dhaliwal, who was featured in a blog for Scientific American , is an expert in “clinical reasoning.” This is a type of applied problem-solving that uses science to solve problems. He uses a four-step method as a guide. Pioneered by mathematician George Polya, the steps are: understanding, creating a plan, seeing the plan through, and looking back to learn from the solution. Dhaliwal believes the key to good problem-solving is finding solutions that best match the problem. Science helps him get there.

#3. Science has many benefits for young students

When they’re young, students are building lots of skills they’ll need later in life. Science helps with many of them, such as clear communication , strong focus, and good organization. Studies show that students usually first become interested in STEM when they’re in elementary school . Supporting that interest helps kids build confidence in scientific subjects. This can lead to more opportunities down the road.

#4. Science led to the creation of technologies we use every day

Science isn’t limited to the study of the natural world, disease, or human lifespans. Without science, we wouldn’t have technologies like computers, the internet, cars, and so on. These inventions transformed how humans live in the world, including how we travel, how we communicate, and how we learn. These inventions in turn facilitate new scientific discoveries and innovations, like DNA sequencing, space exploration, artificial intelligence , and more!

#5. Science careers pay well

Science matters because it can be a lucrative career. There’s a wide variety of fields where science is applicable, such as medicine and computer science. Some careers are accessible with just a bachelor’s degree, though the best-paying jobs typically ask for more education . Scientists who work for federal governments tend to make the most, though it depends on where you’re from. For many people, science is a way to build generational wealth and end cycles of poverty.

#6. Science helps us live longer

The link between scientific advancements and longer life expectancy for humans is impossible to ignore. Without an understanding of germs or effective medical treatments, humans in the past were extremely vulnerable. In Europe from the 1500s to the 1800s, people could expect to live between 30 and 40 years. In 2019, people in Europe had a life expectancy of around 80 years old. There are many reasons for this increase – including better nutrition and better medicine – but they’re all connected to science in one way or another.

#7. Science gives us cleaner drinking water

Humans need water to live, but when water isn’t clean, it can be deadly. Contaminated water and poor sanitation can spread diseases like typhoid, polio, and cholera. Cases of cholera , which is caused by bacteria, have been recorded as far back as the 4th century BCE. Between 1852-1923, four cholera pandemics affected the world. The third was the worst and killed 23,000 people in Great Britain in 1854. That was also the year John Snow, a physician, created a map of cases. His scientific research helped him identify the source: contaminated water from a public well. Science has also helped authorities clean up water supplies. The United States, which has some of the safest drinking water in the world, decreased its waterborne diseases significantly by disinfecting community drinking water .

#8. Science reduces child mortality

Humans used to be extremely vulnerable, but the truth is that we’re still vulnerable in a lot of ways. There are still many reasons why a child might not live to become an adult, including poverty and disease. In 2019, the WHO calculated that over 5 million kids under 5 years died of mostly preventable and treatable causes. Because of science, experts can pinpoint the causes (like waterborne illness and malnutrition) and work to change things. Science also helps doctors learn more about pediatric cancer and other threats to a child’s life.

#9. Science informs us about climate change

The Intergovernmental Panel on Climate Change, which was founded in 1988, has been studying climate change for decades. Its most recent findings forecast a grim future. Climate change is both more severe and more widespread than previously thought. Inequality, conflict, and irreversible damage will only intensify without intervention. Without science, we wouldn’t have an understanding of climate change’s effects or even its existence. The greenhouse effect was discovered in the 1820s and by the end of the 19th century, a Swedish scientist reasoned that human-driven C02 emissions raise the global temperature. Without science, we wouldn’t know why the earth was warming or what to do about it.

#10. Science helps us find alternatives to fossil fuels

Science tells us burning fossil fuels causes climate change, but it also helps us find alternatives . The sun, wind, and planet hold a variety of renewable energy sources. Humans have known about the power of the sun and wind for millennia, but modern science has helped us harness it more efficiently and on a much bigger scale. We’ve also discovered energy sources in plants (in the form of biomass) and within the earth itself (geothermal). Technologies like wind farms, electric cars, solar batteries, and more are also a result of science. As climate change worsens, the world needs to commit to the study and use of renewables . Good science is essential.

#11. Science helps us prepare and respond to disasters

More frequent weather-related disasters are one of the consequences of climate change, but science can help us better prepare. An article from the American Academy of Arts & Sciences’ “The Public Face of Science” project gives several examples of how science helps people prepare and respond to disasters. Scientists were responsible for studying the effects of the 2010 Deepwater Horizon oil spill and Hurricane Sandy in 2012. Understanding disasters (both natural and man-made) through a scientific lens helps experts and communities develop better preparation and mitigation plans.

#12. Science lets us study the possibility of life on other planets

Are we alone in the universe? This question has haunted humans for as long as we’ve looked up into the night sky. Science, like the kind that goes on at the SETI Institute , helps us with the answer. The Institute began with NASA’s SETI program (SETI stands for the search for extraterrestrial intelligence) but it’s since grown into 100 scientists and specialists in outreach, administration, and education. Its research uses telescopes, lab research, field expeditions, advanced data analytics, and more. While humans have yet to find evidence of life on other planets, new science like multifrequency receivers, machine learning, and optical telescopes will help researchers refine their search.

#13. Science teaches us about the past

Science is often thought of as a future-focused endeavor, but it can be used to unlock mysteries about the past. Archaeological science , which is the application of scientific techniques to archaeological materials (like bones), helps us understand things about the plants, animals, and humans that came before us. The study of King Tutankhamun is a great example. In 2010, after two years of work, scientists completed the first DNA study of an ancient Egyptian mummy. Using a CAT scanner and DNA analysis, they found evidence of a club foot, cleft palate, and the DNA from the malaria parasite. They determined that King Tut most likely died from complications of a broken leg and malaria. The study also revealed that Tut’s parents were brother and sister, which was common for Egyptian royalty.

#14. Science can be weaponized

Our discussion of science has been positive so far, but it’s important to acknowledge that the field is not a neutral, benevolent force that exists outside of humans. It has a long history of serious flaws, including racism. There’s even a specific term: scientific racism . This is the belief that “race” is a biological reality and that some races are genetically superior. Scientific racism first emerged in the 18th century and was mostly an attempt to understand differences between cultures. European scientists brought all their prejudices and biases with them. As an example, in 1758, Carl Linnaeus divided humans into four main sub-groups. Groups from Asia and Africa were called, respectively, “greedy” and “sluggish.” People from Europe were classified as “light” and “wise.” Scientific racism evolved over the decades and was used to justify horrendous events in history such as the Trans-Atlantic slave trade and the Holocaust. To prevent the weaponization of science, it’s essential to acknowledge its history and combat racism.

#15. Trust (and distrust) in science has big consequences

Public trust in science has been eroding. There are several reasons for that (including science’s history of racism), but one of the more recent drivers comes from oil companies sowing seeds of doubt about climate change. While their own research showed how serious climate change was, their public stance was “The data is inconclusive.” This doubt didn’t remain walled within climate change issues. It’s spread to every area of society, including public health. We can see the consequences of distrust in science everywhere. Social media algorithms fuel misinformation and the truth can’t keep up. If the world hopes to continue to reap the benefits of science, science literacy and public trust must be prioritized.

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Is Psychology a Science?

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Psychology is a science because it employs systematic methods of observation, experimentation, and data analysis to understand and predict behavior and mental processes, grounded in empirical evidence and subjected to peer review.

Science uses an empirical approach. Empiricism (founded by John Locke) states that the only source of knowledge is our senses – e.g., sight, hearing, etc.

In psychology, empiricism refers to the belief that knowledge is derived from observable, measurable experiences and evidence, rather than from intuition or speculation.

This was in contrast to the existing view that knowledge could be gained solely through the powers of reason and logical argument (known as rationalism).  Thus, empiricism is the view that all knowledge is based on or may come from experience.

Through gaining knowledge through experience, the empirical approach quickly became scientific and greatly influenced the development of physics and chemistry in the 17th and 18th centuries.

The idea that knowledge should be gained through experience, i.e., empirically, turned into a method of inquiry that used careful observation and experiments to gather facts and evidence.

The nature of scientific inquiry may be thought of at two levels:

1. That to do with theory and the foundation of hypotheses. 2. And actual empirical methods of inquiry (i.e. experiments, observations)

The prime empirical method of inquiry in science is the experiment.

The key features of the experiment are control over variables ( independent, dependent , and extraneous ), careful, objective measurement, and establishing cause and effect relationships.

Features of Science

Empirical evidence.

• Refers to data being collected through direct observation or experiment.
• Empirical evidence does not rely on argument or belief.
• Instead, experiments and observations are carried out carefully and reported in detail so that other investigators can repeat and attempt to verify the work.

Objectivity

• Researchers should remain value-free when studying; they should try to remain unbiased in their investigations. I.e., Researchers are not influenced by personal feelings and experiences.
• Objectivity means that all sources of bias are minimized and that personal or subjective ideas are eliminated. The pursuit of science implies that the facts will speak for themselves, even if they differ from what the investigator hoped.
• All extraneous variables need to be controlled to establish the cause (IV) and effect (DV).

Hypothesis testing

• E.g., a statement made at the beginning of an investigation that serves as a prediction and is derived from a theory. There are different types of hypotheses (null and alternative), which need to be stated in a form that can be tested (i.e., operationalized and unambiguous).

Replication

• This refers to whether a particular method and finding can be repeated with different/same people and/or on different occasions to see if the results are similar.
• If a dramatic discovery is reported, but other scientists cannot replicate it, it will not be accepted.
• If we get the same results repeatedly under the same conditions, we can be sure of their accuracy beyond a reasonable doubt.
• This gives us confidence that the results are reliable and can be used to build up a body of knowledge or a theory: which is vital in establishing a scientific theory.

Predictability

• We should aim to be able to predict future behavior from the findings of our research.

The Scientific Process

Before the twentieth century, science largely used induction principles – making discoveries about the world through accurate observations, and formulating theories based on the regularities observed.

Newton’s Laws are an example of this. He observed the behavior of physical objects (e.g., apples) and produced laws that made sense of what he observed.

The scientific process is now based on the hypothetico-deductive model proposed by Karl Popper (1935).  Popper suggested that theories/laws about the world should come first, and these should be used to generate expectations/hypotheses, which observations and experiments can falsify.

As Popper pointed out, falsification is the only way to be certain: ‘No amount of observations of white swans can allow the conclusion that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.

Darwin’s theory of evolution is an example of this. He formulated a theory and tested its propositions by observing animals in nature.  He specifically sought to collect data to prove his theory / disprove it.

Thomas Kuhn argued that science does not evolve gradually towards truth, science has a paradigm that remains constant before going through a paradigm shift when current theories can’t explain some phenomenon, and someone proposes a new theory. Science tends to go through these shifts; therefore, psychology is not a science as it has no agreed paradigm.

There are many conflicting approaches, and the subject matter of Psychology is so diverse; therefore, researchers in different fields have little in common.

Psychology is really a very new science, with most advances happening over the past 150 years or so.  However, it can be traced back to ancient Greece, 400 – 500 years BC.  The emphasis was a philosophical one, with great thinkers such as Socrates influencing Plato, who in turn influenced Aristotle.

Plato argued that there was a clear distinction between body and soul, believed very strongly in the influence of individual differences on behavior, and played a key role in developing the notion of “mental health,” believing that the mind needed stimulation from the arts to keep it alive.

Aristotle firmly believed that the body strongly affected the mind – you might say he was an early biopsychologist.

Psychology as a science took a “back seat” until Descartes (1596 – 1650) wrote in the 17th century. He believed strongly in the concept of consciousness, maintaining that it was that that separated us from animals.

He did, however, believe that our bodies could influence our consciousness and that the beginnings of these interactions were in the pineal gland – we know now that this is probably NOT the case!

From this influential work came other important philosophies about psychology, including the work by Spinoza (1632 – 1677) and Leibnitz (1646 – 1716). But there still was no single, scientific, unified psychology as a separate discipline (you could certainly argue that there still isn’t”t!).

When asked, “Who is the parent of psychology?” many people answer, “Freud.” Whether this is the case or not is open to debate, but if we were to ask who the parent of experimental psychology is, few would likely respond similarly.  So, where did modern experimental psychology come from, and why?

Psychology took so long to emerge as a scientific discipline because it needed time to consolidate.  Understanding behavior, thoughts, and feelings are not easy, which may explain why it was largely ignored between ancient Greek times and the 16th century.

But tired of years of speculation, theory, and argument, and bearing in mind Aristotle’s plea for scientific investigation to support the theory, psychology as a scientific discipline began to emerge in the late 1800s.

Wilheim Wundt developed the first psychology lab in 1879.  Introspection was used, but systematically (i.e., methodologically). It was really a place from which to start thinking about how to employ scientific methods to investigate behavior.

The classic movement in psychology to adopt these strategies was the behaviorists, who were renowned for relying on controlled laboratory experiments and rejecting any unseen or subconscious forces as causes of behavior. 

And later, cognitive psychologists adopted this rigorous (i.e., careful), scientific, lab-based approach.

Psychological Approaches

Psychoanalysis has great explanatory power and understanding of behavior. Still, it has been accused of only explaining behavior after the event, not predicting what will happen in advance, and being unfalsifiable.

Some have argued that psychoanalysis has approached the status more of a religion than a science. Still, it is not alone in being accused of being unfalsifiable (evolutionary theory has, too – why is anything the way it is? Because it has evolved that way!), and like theories that are difficult to refute – the possibility exists that it is actually right.

Kline (1984) argues that psychoanalytic theory can be broken down into testable hypotheses and tested scientifically. For example, Scodel (1957) postulated that orally dependent men would prefer larger breasts (a positive correlation) but, in fact, found the opposite (a negative correlation).

Although Freudian theory could be used to explain this finding (through reaction formation – the subject showing exactly the opposite of their unconscious impulses!), Kline has nevertheless pointed out that no significant correlation would have refuted the theory.

Behaviorism has parsimonious (i.e., economic / cost-cutting) theories of learning, using a few simple principles (reinforcement, behavior shaping, generalization, etc.) to explain a wide variety of behavior from language acquisition to moral development.

It advanced bold, precise, and refutable hypotheses (such as Thorndike’s law of effect ) and possessed a hard core of central assumptions such as determinism from the environment (it was only when this assumption faced overwhelming criticism by the cognitive and ethological theorists that the behaviorist paradigm/model was overthrown).

Behaviorists firmly believed in the scientific principles of determinism and orderliness. They thus came up with fairly consistent predictions about when an animal was likely to respond (although they admitted that perfect prediction for any individual was impossible).

The behaviorists used their predictions to control the behavior of both animals (pigeons trained to detect life jackets) and humans (behavioral therapies), and indeed Skinner , in his book Walden Two (1948), described a society controlled according to behaviorist principles.

Cognitive psychology – adopts a scientific approach to unobservable mental processes by advancing precise models and conducting experiments on behavior to confirm or refute them.

Full understanding, prediction, and control in psychology are probably unobtainable due to the huge complexity of environmental, mental, and biological influences upon even the simplest behavior (i.e., all extraneous variables cannot be controlled).

You will see, therefore, that there is no easy answer to the question, “is psychology a science?”. But many approaches of psychology do meet the accepted requirements of the scientific method, whilst others appear to be more doubtful in this respect.

Alternatives

However, some psychologists argue that psychology should not be a science. There are alternatives to empiricism, such as rational research, argument, and belief.

The humanistic approach (another alternative) values private, subjective conscious experience and argues for the rejection of science.

The humanistic approach argues that objective reality is less important than a person’s subjective perception and subjective understanding of the world. Because of this, Carl Rogers and Maslow placed little value on scientific psychology, especially using the scientific laboratory to investigate human and other animal behavior.

A person’s subjective experience of the world is an important and influential factor in their behavior. Only by seeing the world from the individual’s point of view can we really understand why they act the way they do. This is what the humanistic approach aims to do.

Humanism is a psychological perspective that emphasizes the study of the whole person. Humanistic psychologists look at human behavior not only through the eyes of the observer but through the eyes of the person doing the behavior. Humanistic psychologists believe that an individual’s behavior is connected to his inner feelings and self-image.

The humanistic approach in psychology deliberately steps away from a scientific viewpoint, rejecting determinism in favor of free will, aiming to arrive at a unique and in-depth understanding. The humanistic approach does not have an orderly set of theories (although it does have some core assumptions).

It is not interested in predicting and controlling people’s behavior – the individuals themselves are the only ones who can and should do that.

Miller (1969), in “Psychology as a Means of Promoting Human Welfare,” criticizes the controlling view of psychology, suggesting that understanding should be the main goal of the subject as a science since he asks who will do the controlling and whose interests will be served by it?

Humanistic psychologists rejected a rigorous scientific approach to psychology because they saw it as dehumanizing and unable to capture the richness of conscious experience.

In many ways, the rejection of scientific psychology in the 1950s, 1960s, and 1970s was a backlash to the dominance of the behaviorist approach in North American psychology.

Common Sense Views of Behavior

In certain ways, everyone is a psychologist. This does not mean that everyone has been formally trained to study and be trained in psychology. 

People have common sense views of the world, of other people, and of themselves. These common-sense views may come from personal experience, from our upbringing as a child, and through culture, etc.

People have common-sense views about the causes of their own and other people’s behavior, personality characteristics they and others possess, what other people should do, how to bring up your children, and many more aspects of psychology.

Informal psychologists acquire common-sense knowledge in a rather subjective (i.e., unreliable) and anecdotal way.  Common-sense views about people are rarely based on systematic (i.e., logical) evidence and are sometimes based on a single experience or observation.

Racial or religious prejudices may reflect what seems like common sense within a group of people. However, prejudicial beliefs rarely stand up to what is actually the case.

Common sense, then, is something that everybody uses in their day-to-day lives, guides decisions and influences how we interact with one another.

However, because it is not based on systematic evidence or derived from scientific inquiry, it may be misleading and lead to one group of people treating others unfairly and in a discriminatory way.

Limitations of Scientific Psychology

Despite having a scientific methodology worked out (we think), some further problems and arguments doubt psychology is ever a science.

Limitations may refer to the subject matter (e.g., overt behavior versus subjective, private experience), objectivity, generality, testability, ecological validity, ethical issues, and philosophical debates, etc.

Science assumes that there are laws of human behavior that apply to each person. Therefore, science takes both a deterministic and reductionist approach.

Science studies overt behavior because overt behavior is objectively observable and can be measured, allowing different psychologists to record behavior and agree on what has been observed. This means that evidence can be collected to test a theory about people.

Scientific laws are generalizable, but psychological explanations are often restricted to specific times and places. Because psychology studies (mostly) people, it studies (indirectly) the effects of social and cultural changes on behavior.

Psychology does not go on in a social vacuum. Behavior changes over time and in different situations. These factors, and individual differences, make research findings reliable for a limited time only.

Are traditional scientific methods appropriate for studying human behavior? When psychologists operationalize their IV, it is highly likely that this is reductionist, mechanistic, subjective, or just wrong.

Operationalizing variables refers to how you will define and measure a specific variable as it is used in your study. For example, a biopsychologist may operationalize stress as an increased heart rate. Still, it may be that in doing this, we are removed from the human experience of what we are studying. The same goes for causality.

Experiments are keen to establish that X causes Y, but taking this deterministic view means that we ignore extraneous variables and the fact that at a different time, in a different place, we probably would not be influenced by X. There are so many variables that influence human behavior that it is impossible to control them effectively. The issue of ecological validity ties in really nicely here.

Objectivity is impossible. It is a huge problem in psychology, as it involves humans studying humans, and it is very difficult to study people’s behavior in an unbiased fashion.

Moreover, in terms of a general philosophy of science, we find it hard to be objective because a theoretical standpoint influences us (Freud is a good example). The observer and the observed are members of the same species are this creates problems of reflectivity.

A behaviorist would never examine a phobia and think in terms of unconscious conflict as a cause, just like Freud would never explain it as a behavior acquired through operant conditioning.

This particular viewpoint that a scientist has is called a paradigm (Kuhn, 1970). Kuhn argues that most scientific disciplines have one predominant paradigm that the vast majority of scientists subscribe to.

Anything with several paradigms (e.g., models – theories) is a pre-science until it becomes more unified. With a myriad of paradigms within psychology, it is not the case that we have any universal laws of human behavior. Kuhn would most definitely argue that psychology is not a science.

Verification (i.e., proof) may be impossible. We can never truly prove a hypothesis; we may find results to support it until the end of time, but we will never be 100% confident that it is true.

It could be disproved at any moment. The main driving force behind this particular grumble is Karl Popper, the famous philosopher of science and advocator of falsificationism.

Take the famous Popperian example hypothesis: “All swans are white.” How do we know for sure that we will not see a black, green, or hot pink swan in the future? So even if there has never been a sighting of a non-white swan, we still haven’t really proven our hypothesis.

Popper argues that the best hypotheses are those which we can falsify – disprove. If we know something is not true, then we know something for sure.

Testability: much of the subject matter in psychology is unobservable (e.g., memory) and, therefore, cannot be accurately measured. The fact that there are so many variables that influence human behavior that it is impossible to control the variables effectively.

So, are we any closer to understanding a) what science is and b) if psychology is a science? Unlikely. There is no definitive philosophy of science and no flawless scientific methodology.

When people use the term “Scientific,” we all have a general schema of what they mean, but when we break it down in the way that we just have done, the picture is less certain. What is science? It depends on your philosophy. Is psychology a science? It depends on your definition. So – why bother, and how do we conclude all this?

Slife and Williams (1995) have tried to answer these two questions:

1) We must at least strive for scientific methods because we need a rigorous discipline. If we abandon our search for unified methods, we’ll lose a sense of what psychology is (if we knew it in the first place).

2) We need to keep trying to develop scientific methods that are suitable for studying human behavior – it may be that the methods adopted by the natural sciences are not appropriate for us.

Further Information

• Psychology as a Science (PDF)

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Understanding Science

How science REALLY works...

• Understanding Science 101

An overview

To understand what ​​ science is, just look around you. What do you see? Perhaps your hand on the mouse, a computer screen, papers, ballpoint pens, the family cat, the sun shining through the window …. Science is, in one sense, our knowledge of all that — all the stuff that is in the universe, including the tiniest subatomic particles in a single atom of the metal in your computer’s circuits, the nuclear reactions that formed the immense ball of gas that is our sun, and the complex chemical interactions and electrical fluctuations within your own body that allow you to read and understand these words. But science is not just a collection of knowledge. Just as importantly, science is also a reliable process by which we learn about all that stuff in the universe. And science is different from many other ways of learning because of the way it is done. Science relies on ​​ testing ideas with ​​ evidence gathered from the ​​ natural world . This website will help you learn more about science as a process of learning about the natural world and access the parts of science that affect your life.

Science helps to satisfy the natural curiosity with which we are all born: Why is the sky blue? How did the leopard get its spots? What is a solar eclipse? With science, we can answer such questions without resorting to magical explanations. And science can lead to technological advances, as well as helping us learn about enormously important and useful topics, such as our health, the environment, and natural hazards. Without science, the modern world would not be modern at all. Still, we have so much to learn. Millions of scientists all over the world are working to solve different parts of the puzzle of how the universe works, peering into its nooks and crannies and deploying their microscopes, telescopes, and other tools to unravel its secrets.

Science is complex and multi-faceted, but the most important characteristics of science are straightforward:

• Science is a way of learning about what is in the natural world, how the natural world works, and how the natural world got to be the way it is. It is not simply a collection of facts ; rather it is a path to understanding.
• Science focuses exclusively on the natural world and does not deal with supernatural explanations.
• Although scientists work in many different ways, all science relies on testing ideas by figuring out what expectations are generated by an idea and making observations to find out whether those expectations hold true.
• Accepted scientific ideas are reliable because they have been subjected to rigorous testing. But, as new evidence is acquired and new perspectives emerge, these ideas can be revised.
• Science is a community endeavor. It relies on a system of checks and balances, which helps ensure that science moves in the direction of greater accuracy and understanding. This system is facilitated by diversity within the scientific community, which offers a broad range of perspectives on scientific ideas.

To many, science may seem like an arcane, ivory-towered institution — but that impression is based on a misunderstanding of science. In fact:

• Science affects your life everyday in all sorts of different ways.
• Science can be fun and is accessible to everyone.
• You are probably already using scientific thinking in your everyday life – maybe without even knowing it.
• Anyone can “do” science by investigating questions scientifically.

Where to begin?

Here are some places you may want to start your investigation:

• What is science? Find out what makes science science .
• How does it work? Probe the nuts and bolts of the process of science .
• Why is it important? Learn how science affects your life everyday and how you can apply an understanding of the nature of science in your everyday life.

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Six Reasons Why Research is Important

Everyone conducts research in some form or another from a young age, whether news, books, or browsing the Internet. Internet users come across thoughts, ideas, or perspectives - the curiosity that drives the desire to explore. However, when research is essential to make practical decisions, the nature of the study alters - it all depends on its application and purpose. For instance, skilled research offered as a  research paper service  has a definite objective, and it is focused and organized. Professional research helps derive inferences and conclusions from solving problems. visit the HB tool services for the amazing research tools that will help to solve your problems regarding the research on any project.

What is the Importance of Research?

The primary goal of the research is to guide action, gather evidence for theories, and contribute to the growth of knowledge in data analysis. This article discusses the importance of research and the multiple reasons why it is beneficial to everyone, not just students and scientists.

On the other hand, research is important in business decision-making because it can assist in making better decisions when combined with their experience and intuition.

Reasons for the Importance of Research

• Acquire Knowledge Effectively
• Research helps in problem-solving
• Provides the latest information
• Builds credibility
• Helps in business success
• Discover and Seize opportunities

1-  Acquire Knowledge Efficiently through Research

The most apparent reason to conduct research is to understand more. Even if you think you know everything there is to know about a subject, there is always more to learn. Research helps you expand on any prior knowledge you have of the subject. The research process creates new opportunities for learning and progress.

2- Research Helps in Problem-solving

Problem-solving can be divided into several components, which require knowledge and analysis, for example,  identification of issues, cause identification,  identifying potential solutions, decision to take action, monitoring and evaluation of activity and outcomes.

You may just require additional knowledge to formulate an informed strategy and make an informed decision. When you know you've gathered reliable data, you'll be a lot more confident in your answer.

3- Research Provides the Latest Information

Research enables you to seek out the most up-to-date facts. There is always new knowledge and discoveries in various sectors, particularly scientific ones. Staying updated keeps you from falling behind and providing inaccurate or incomplete information. You'll be better prepared to discuss a topic and build on ideas if you have the most up-to-date information. With the help of tools and certifications such as CIRS , you may learn internet research skills quickly and easily. Internet research can provide instant, global access to information.

4- Research Builds Credibility

Research provides a solid basis for formulating thoughts and views. You can speak confidently about something you know to be true. It's much more difficult for someone to find flaws in your arguments after you've finished your tasks. In your study, you should prioritize the most reputable sources. Your research should focus on the most reliable sources. You won't be credible if your "research" comprises non-experts' opinions. People are more inclined to pay attention if your research is excellent.

5-  Research Helps in Business Success

R&D might also help you gain a competitive advantage. Finding ways to make things run more smoothly and differentiate a company's products from those of its competitors can help to increase a company's market worth.

6-  Research Discover and Seize Opportunities

People can maximize their potential and achieve their goals through various opportunities provided by research. These include getting jobs, scholarships, educational subsidies, projects, commercial collaboration, and budgeted travel. Research is essential for anyone looking for work or a change of environment. Unemployed people will have a better chance of finding potential employers through job advertisements or agencies. 

How to Improve Your Research Skills

Start with the big picture and work your way down.

It might be hard to figure out where to start when you start researching. There's nothing wrong with a simple internet search to get you started. Online resources like Google and Wikipedia are a great way to get a general idea of a subject, even though they aren't always correct. They usually give a basic overview with a short history and any important points.

Identify Reliable Source

Not every source is reliable, so it's critical that you can tell the difference between the good ones and the bad ones. To find a reliable source, use your analytical and critical thinking skills and ask yourself the following questions: Is this source consistent with other sources I've discovered? Is the author a subject matter expert? Is there a conflict of interest in the author's point of view on this topic?

Validate Information from Various Sources

Take in new information.

The purpose of research is to find answers to your questions, not back up what you already assume. Only looking for confirmation is a minimal way to research because it forces you to pick and choose what information you get and stops you from getting the most accurate picture of the subject. When you do research, keep an open mind to learn as much as possible.

Facilitates Learning Process

Learning new things and implementing them in daily life can be frustrating. Finding relevant and credible information requires specialized training and web search skills due to the sheer enormity of the Internet and the rapid growth of indexed web pages. On the other hand, short courses and Certifications like CIRS make the research process more accessible. CIRS Certification offers complete knowledge from beginner to expert level. You can become a Certified Professional Researcher and get a high-paying job, but you'll also be much more efficient and skilled at filtering out reliable data. You can learn more about becoming a Certified Professional Researcher.

Stay Organized

You'll see a lot of different material during the process of gathering data, from web pages to PDFs to videos. You must keep all of this information organized in some way so that you don't lose anything or forget to mention something properly. There are many ways to keep your research project organized, but here are a few of the most common:  Learning Management Software , Bookmarks in your browser, index cards, and a bibliography that you can add to as you go are all excellent tools for writing.

Make Use of the library's Resources

If you still have questions about researching, don't worry—even if you're not a student performing academic or course-related research, there are many resources available to assist you. Many high school and university libraries, in reality, provide resources not only for staff and students but also for the general public. Look for research guidelines or access to specific databases on the library's website. Association of Internet Research Specialists enjoys sharing informational content such as research-related articles , research papers , specialized search engines list compiled from various sources, and contributions from our members and in-house experts.

of Conducting Research

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Why research is considered a science?

Scientific research relies on the application of the scientific method, a harnessing of curiosity . This research provides scientific information and theories for the explanation of the nature and the properties of the world. It makes practical applications possible. Scientific research is funded by public authorities, by charitable organizations and by private groups, including many companies. Scientific research can be subdivided into different classifications according to their academic and application disciplines. Scientific research is a widely used criterion for judging the standing of an academic institution, such as business schools, but some argue that such is an inaccurate assessment of the institution, because the quality of research does not tell about the quality of teaching (these do not necessarily correlate totally)

Astronomy is the oldest science for our ancients either drew or made structures according to seasons or movements of celestial bodies in our Universe. An excellent example is Stonehenge in Wiltshire, England has been dated to 3100 B.C.E. in which one of the functions is to show when the solstices occurs (Summer and Winter).

Agriculture is considered to be a science since it is involves different aspects of Biology . It is also considered as science as it aims at studying an analyzing different regions and their soil properties.

Research is considered scientific because it deals with facts.

Add your answer:

Why is considered a science?

considered is not science. It is a word

Where can you get science tests from?

In your knowledge, what you learned from school, and research, research, research. That'll give you the answers.

What are the different roles in the science research team?

Are phobias considered science.

Phobias are an aspect of psychology, which is considered a science. So yes, phobias are a science of sorts

What is the difference between natural science research and social science research?

A natural environment is the environment which surrounds or that we live in while a social environment is the environment of people that surround us.

What is the Mediterranean Sea sometimes called the Roman Lake?

They called it Mare Nostrum (Our Sea) because they controlled most of the territories surrounding it and the islands in it.Another term used by historians is to call it a "Roman Lake".

What research would be considered basic science?

Basic research in chemistry means an exploration into an element of chemistry. This can be related to the human body or how chemicals interact for instance.

One of the basic differences between science and a pseudoscience is the lack of?

One of the basic differences between science and pseudoscience is a lack of empirical research. Empirical research must meet the rigors of validity and reliability criteria to be considered science.

Are the existences of tachyons considered to be fringe science?

Depends what you mean by "fringe." Some PhD's in major institutions are doing research into tachyons. But it is not considered an area that a LOT of research effort is being or should be done.

Why sociology is considered a social science?

Sociology is considered a science because it involves systematic methods of empirical research, analysis of data as well as assessment of data. It also asks questions which van be quantified.

Is research an application of science?

Yes. In a sense Science is essentially research.

What is a popular subject of research in environmental science?

Popular subjects include Ecology chemistry and biology. Anything that is related to the earth in a scientific way is considered environmental science.

When was Science Research Associates created?

Science Research Associates was created in 1938.

What does the reading program sra stand for?

Science Research Associates

When was Social Science Research Council created?

Social Science Research Council was created in 1923.

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Everyone on Earth is touched by the effects of climate change, such as hotter temperatures, shifts in rain patterns, and sea level rise. Collecting climate data helps communities better plan for these changes and build more resilience to them.

The International Space Station, one of dozens of NASA missions contributing to this effort, has multiple instruments collecting various types of climate-related data. Because the station’s orbit passes over 90 percent of Earth’s population and circles the planet 16 times each day, these instruments have views of multiple locations at different times of day and night. The data inform climate decisions and help scientists understand and solve the challenges created by climate change.

While crew members have little involvement in the ongoing operation of these instruments, they do play a critical role in unpacking hardware when it arrives at the space station and in assembling and installing the instruments via spacewalks or using the station’s robotic arm.

One investigation on the orbiting lab that contributes to efforts to monitor and address climate change is ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station ( ECOSTRESS ). It provides thermal infrared measurements of Earth’s surface that help answer questions about water stress in plants and how specific regions respond to climate change. Research confirmed the accuracy of ECOSTRESS surface estimates 1 and found that the process of photosynthesis in plants begins to fail at 46.7 degrees C (114 degrees F). 2 Average temperatures have increased 0.5 degrees C per decade in some tropical regions, and temperature extremes are becoming more pronounced. Rainforests are a primary producer of oxygen and, without sufficient mitigation of the effects of climate change, leaf temperatures in these tropical forests soon could approach this failure threshold.

The Total and Spectral Solar Irradiance Sensor ( TSIS ) measures total solar irradiance (TSI) and solar spectral irradiance (SSI). TSI is the total solar energy input to Earth and SSI measures the Sun’s energy in individual wavelengths. Energy from the Sun drives atmospheric and oceanic circulations on Earth, and knowing its magnitude and variability is essential to understanding Earth’s climate. Researchers verified the instrument’s performance and showed that it made more accurate measurements than previous instruments. 3,4 TSIS maintains a continuity of nearly 40 years of data on solar irradiance from space-based observations.

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The Global Ecosystem Dynamics Investigation ( GEDI ) observes global forests and topography using light detection and ranging (lidar). These observations could provide insight into important carbon and water cycling processes, biodiversity, and habitat. One study used GEDI data to estimate pan-tropical and temperate biomass densities at the national level for every country observed and the sub-national level for the United States. 5

Earth Surface Mineral Dust Source Investigation ( EMIT ) determines the type and distribution of minerals in the dust of Earth’s arid regions using an imaging spectrometer. Mineral dust affects local warming and cooling, air quality, rate of snow melt, and ocean plankton growth. Researchers demonstrated that data from EMIT also can be used to identify and monitor specific sources of methane and carbon dioxide emissions. Carbon dioxide and methane are the primary human-caused drivers of climate change. Increasing emissions in areas with poor reporting requirements create significant uncertainty in the global carbon budget. 6 The high spatial resolution of EMIT data could allow precise monitoring even of sources that are close together.

The station’s Orbiting Carbon Observatory-3 ( OCO-3 ) collects data on global carbon dioxide during sunlit hours, mapping emissions of targeted local hotspots. This type of satellite-based remote sensing helps assess and verify emission reductions included in national and global plans and agreements. Monitoring by OCO-3 and the Italian Space Agency’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of 30 coal-fired power plants between 2021 and 2022 showed agreement with on-site observations. 7 This result suggests that under the right conditions, satellites can provide reliable estimates of emissions from discreet sources. Combustion for power and other industrial uses account for an estimated 59% of global human-caused carbon dioxide emissions.

The Stratospheric Aerosol and Gas Experiment III-ISS ( SAGE III-ISS ) measures ozone and other gases and tiny particles in the atmosphere, called aerosols, that together act as Earth’s sunscreen. The instrument can distinguish between clouds and aerosols in the atmosphere. A study showed that aerosols dominate Earth’s tropical upper troposphere and lower stratosphere, a transition region between the two atmospheric levels. Continuous monitoring and identification of these layers of the atmosphere helps quantify their effect on Earth’s climate. 8

An early remote sensing system, ISS SERVIR Environmental Research and Visualization System ( ISERV ), automatically took images of Earth to help scientists assess and monitor disasters and other significant events. Researchers reported that this type of Earth observation is critical for applications such as mapping land use and assessing carbon biomass and ocean health. 9

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Search this database of scientific experiments to learn more about those mentioned above.

1 Weidberg N, Lopez Chiquillo L, Roman S, Roman M, Vazquez E, et al. Assessing high resolution thermal monitoring of complex intertidal environments from space: The case of ECOSTRESS at Rias Baixas, NW Iberia. Remote Sensing Applications: Society and Environment. 2023 November; 32101055. DOI: 10.1016/j.rsase.2023.101055.

2 Doughty CE, Keany JM, Wiebe BC, Rey-Sanchez C, Carter KR, et al. Tropical forests are approaching critical temperature thresholds. Nature. 2023 August 23; 621105-111. DOI: 10.1038/s41586-023-06391-z.

3 Richard EC, Harber D, Coddington OM, Drake G, Rutkowski J, et al. SI-traceable spectral irradiance radiometric characterization and absolute calibration of the TSIS-1 Spectral Irradiance Monitor (SIM). Remote Sensing. 2020 January; 12(11): 1818. DOI:  10.3390/rs12111818.

4 Coddington OM, Richard EC, Harber D, Pilewskie P, Chance K, et al. The TSIS-1 hybrid solar reference spectrum. Geophysical Research Letters. 2021 April 26; 48(12): e2020GL091709. DOI:  10.1029/2020GL091709

5 Dubayah R, Armston J, Healey S, Bruening JM, Patterson PL, et al. GEDI launches a new era of biomass inference from space. Environmental Research Letters. 2022 August; 17(9): 095001. DOI: 10.1088/1748-9326/ac8694.

6 Thorpe A, Green RD, Thompson DR, Brodrick PG, Chapman DK, et al. Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space. Science Advances. 2023 November 17; 9(46): eadh2391. DOI: 10.1126/sciadv.adh2391.

7 Cusworth DH, Thorpe A, Miller CE, Ayasse AK, Jiorle R, et al. Two years of satellite-based carbon dioxide emission quantification at the world’s largest coal-fired power plants. Atmospheric Chemistry and Physics. 2023 November 24; 23(22): 14577-14591. DOI: 10.5194/acp-23-14577-2023.

8 Bhatta S, Pandit AK, Loughman R, Vernier J. Three-wavelength approach for aerosol-cloud discrimination in the SAGE III/ISS aerosol extinction dataset. Applied Optics. 2023 May; 62(13): 3454-3466. DOI: 10.1364/AO.485466 .

9 Kansakar P, Hossain F. A review of applications of satellite earth observation data for global societal benefit and stewardship of planet earth. Space Policy. 2016 May; 3646-54.

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‘Like a film in my mind’: hyperphantasia and the quest to understand vivid imaginations

Research that aims to explain why some people experience intense visual imagery could lead to a better understanding of creativity and some mental disorders

W illiam Blake’s imagination is thought to have burned with such intensity that, when creating his great artworks, he needed little reference to the physical world. While drawing historical or mythical figures, for instance, he would wait until the “spirit” appeared in his mind’s eye. The visions were apparently so detailed that Blake could sketch as if a real person were sitting before him.

Like human models, these imaginary figures could sometimes act temperamentally. According to Blake biographer John Higgs , the artist could become frustrated when the object of his inner gaze casually changed posture or left the scene entirely. “I can’t go on, it is gone! I must wait till it returns,” Blake would declaim.

Such intense and detailed imaginations are thought to reflect a condition known as hyperphantasia, and it may not be nearly as rare as we once thought, with as many as one in 30 people reporting incredibly vivid mind’s eyes.

Just consider the experiences of Mats Holm, a Norwegian hyperphantasic living in Stockholm. “I can essentially zoom out and see the entire city around me, and I can fly around inside that map of it,” Holm tells me. “I have a second space in my mind where I can create any location.”

This once neglected form of neurodiversity is now a topic of scientific study, which could lead to insights into everything from creative inspiration to mental illnesses such as post-traumatic stress disorder and psychosis.

Francis Galton – better known as a racist and the “father of eugenics” – was the first scientist to recognise the enormous variation in people’s visual imagery. In 1880, he asked participants to rate the “illumination, definition and colouring of your breakfast table as you sat down to it this morning”. Some people reported being completely unable to produce an image in the mind’s eye, while others – including his cousin Charles Darwin – could picture it extraordinarily clearly.

“Some objects quite defined. A slice of cold beef, some grapes and a pear, the state of my plate when I had finished and a few other objects are as distinct as if I had photos before me,” Darwin wrote to Galton.

Unfortunately, Galton’s findings failed to fire the imagination of scientists at the time. “The psychology of visual imagery was a very big topic, but the existence of people at the extremes somehow disappeared from view,” says Prof Adam Zeman at Exeter University. It would take more than a century for psychologists such as Zeman to take up where Galton left off.

Even then, much of the initial research focused on the poorer end of the spectrum – people with aphantasia , who claim to lack a mind’s eye. Within the past five years, however, interest in hyperphantasia has started to grow, and it is now a thriving area of research.

To identify where people lie on the spectrum, researchers often use the Vividness of Visual Imagery Questionnaire (VVIQ), which asks participants to visualise a series of 16 scenarios, such as “the sun rising above the horizon into a hazy sky” and then report on the level of detail that they “see” in a five-point scale. You can try it for yourself. When you picture that sunrise, which of the following statements best describes your experience?

1. No image at all, you only “know” that you are thinking of the object 2. Vague and dim 3. Moderately clear and lively 4. Clear and reasonably vivid 5. Perfectly clear and as vivid as real seeing

The final score is the sum of all 16 responses, with a maximum of 80 points. In large surveys, most people score around 55 to 60 . Around 1% score just 16; they are considered to have extreme aphantasia; 3%, meanwhile, achieve a perfect score of 80, which is extreme hyperphantasia.

The VVIQ is a relatively blunt tool, but Reshanne Reeder, a lecturer at Liverpool University, has now conducted a series of in-depth interviews with hyperphantasic people – research that helps to delineate the peculiarities of their inner lives. “As you talk to them, you start to realise that this is a very different experience from most people’s experience,” she says. “It’s extremely immersive, and their imagery affects them very emotionally.”

Some people with hyperphantasia are able to merge their mental imagery with their view of the world around them. Reeder asked participants to hold out a hand and then imagine an apple sitting in their palm. Most people feel that the scene in front of their eyes is distinct from that inside their heads. “But a lot of people with hyperphantasia – about 75% – can actually see an apple in the hand in front of them. And they can even feel its weight.”

As you might expect, these visual abilities can influence career choices. “Aphantasia does seem to bias people to work in sciences, maths or IT – those Stem professions – whereas hyperphantasia nudges people to work in what are traditionally called creative professions,” says Zeman. “Though there are many exceptions.”

Reeder recalls one participant who uses her hyperphantasia to fuel her writing. “She said she doesn’t even have to think about the stories that she’s writing, because she can see the characters right in front of her, acting out their parts,” Reeder recalls.

H yperphantasia can also affect people’s consumption of art. Novels, for example, become a cinematic experience. “For me, the story is like a film in my mind,” says Geraldine van Heemstra , an artist based in London. Holm offers the same description. “When I listen to an audiobook, I’m running a movie in my head.”

This is not always an advantage. Laura Lewis Alvarado, a union worker who is also based in London, describes her disappointment at watching The Golden Compass, the film adaptation of the first part of Philip Pullman’s His Dark Materials . “I already had such a clear idea of how every character looked and acted,” she says. The director’s choices simply couldn’t match up.

Zeman’s research suggests that people with hyperphantasia enjoy especially rich autobiographical memories. This certainly rings true for Van Heemstra. When thinking of trips in the countryside, she can recall every step of her walks, including seemingly inconsequential details. “I can picture even little things, like if I dropped something and picked it up,” she says.

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Exactly where these abilities come from is unknown. Aphantasia is known to run in families, so it’s reasonable to expect that hyperphantasia may be the same. Like many other psychological traits, our imaginative abilities probably come from a combination of nature and nurture, which will together shape the brain’s development from infancy to old age.

Zeman has taken the first steps to investigate the neurological differences that underpin the striking variation in the mind’s eye. Using fMRI to scan the brains of people at rest, he has found that hyperphantasic people have greater connectivity between the prefrontal cortex, which is involved in “higher-order” thinking such as planning and decision-making, and the areas responsible for visual processing, which lie towards the back of the skull.

“My guess is that if you say ‘apple’ to somebody with hyperphantasia, the linguistic representation of ‘apple’ in the brain immediately transmits the information to the visual system,” says Zeman. “For someone with aphantasia, the word and concept of ‘apple’ operate independently of the visual system, because those connections are weaker.”

Further research will no doubt reveal the nuances in this process. Detailed questionnaires by Prof Liana Palermo at the Magna Graecia University in Catanzaro, Italy, for instance, suggest that there may be two subtypes of vivid imagery . The first is object hyperphantasia, which, as the name suggests, involves the capacity to imagine items in extreme detail.

The second is spatial hyperphantasia, which involves an enhanced ability to picture the orientation of different items relative to one another and perform mental rotations. “They also report a heightened sense of direction,” Palermo says. This would seem to match Holm’s descriptions of the detailed 3D cityscape that allows him to find a route between any two locations.

Many mysteries remain. A large survey by Prof Ilona Kovács, at Eötvös Loránd University in Hungary, suggests that hyperphantasia is far more common among children, and fades across adolescence and into adulthood. She suspects that this may reflect differences in how the brain encodes information. In infancy, our brains store more sensory details, which are slowly replaced by more abstract ideas. “The child’s memories offer a more concrete appreciation of the world,” she says – and it seems that only a small percentage of people can maintain this into later life.

Reeder, meanwhile, is interested in studying the consequences of hyperphantasia for mental health. It is easy to imagine how vivid memories of upsetting events could worsen the symptoms of anxiety or post-traumatic stress disorder, for example.

Reeder is also investigating the ways that people’s mental imagery may influence the symptoms of illnesses such as schizophrenia . She suspects that, if someone is already at risk of psychosis, then hyperphantasia may lead them to experience vivid hallucinations, while aphantasia may increase the risk of non-sensory delusions, such as fears of persecution.

For the moment, this remains an intriguing hypothesis, but Reeder has shown that people with more vivid imagery in daily life are also more susceptible to seeing harmless “ pseudo-hallucinations ” in the laboratory. She asked participants to sit in a darkened room while watching a flickering light on a screen – a set-up that gently stimulates the brain’s visual system. After a few minutes, many people will start to see simple illusions, such as geometric shapes. People with higher VVIQ scores, however, tended to see far more complex scenes – such as a stormy beach, a medieval castle or a volcano. “It was quite psychedelic,” says Lewis Alvarado, who took part in the experiment.

Reeder emphasises that the participants in her study were perfectly able to recognise that these pseudo-hallucinations were figments of their imagination. “If someone never has reality discrimination issues, then I don’t think they’re going to be more prone to psychosis.” For those with mental illness, however, a better understanding of the mind’s eye could offer insights into the patient’s experiences.

For now, Reeder hopes that greater awareness of hyperphantasia will help people to make the most of their abilities. “It’s a skill that could be tapped,” she suggests.

Many of the people I have interviewed are certainly grateful to know a little more about the mind’s eye and the way theirs differs from the average person’s.

Lewis Alvarado, for instance, only came across the term when she was listening to a podcast about William Blake, which eventually led her to contact Reeder. “For the first month or so I couldn’t get it out of my head,” she says. “It’s not something I talk about loads, but I think it has helped me to realise why I experience things more intensely, which is comforting.”

David Robson is the author of The Laws of Connection: 13 Social Strategies That Will Transform Your Life , published by Canongate on 6 June (£18.99). To support the Guardian and Observer , order your copy at guardianbookshop.com . Delivery charges may apply

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Why drafting a successful NFL quarterback remains ‘an inexact science’

The NFL franchise quarterback — the highly coveted symbol of hope, cornerstone of championship aspirations, difference between irrelevance and immortality — is also the rarest of breeds.

Don’t call it tanking, but desperate organizations abandon win-at-all-cost principles with the goal of landing an elite passing prospect with one of the top picks in the NFL Draft . Other quarterback-needy teams routinely mortgage future resources to improve draft positioning. But despite NFL decision-makers’ robust resumes as talent evaluators and extensive research — countless hours of game film, interviews and a gamut of measurables and grading tools — drafting a franchise quarterback remains one of the most challenging exercises they will ever execute.

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“We all evaluated Tom Brady wrong when New England drafted him in the sixth round, and he’s turned out to be one of the best players — not just the best quarterback — the National Football League has ever seen,” Atlanta Falcons coach Raheem Morris said. “So it is continuously an issue for everybody. … We know those things that you go out and try to find, but it’s just a hard position to evaluate.”

• Follow live coverage of the 2024 NFL Draft today

On Thursday, teams will draft the next crop of projected franchise saviors. But these pivotal selections essentially amount to educated guesses about a position often described as the hardest to play in all of professional sports.

In the modern era of the NFL Draft (since 1967), 130 quarterbacks have been selected in the first round. Only 61 of those (46.9 percent) have won a playoff game as a starter, according to NFL Research, and just 58 of those quarterbacks (44.6 percent) have garnered Pro Bowl honors. Just 13 (8.1 percent) won a Super Bowl as a starter, and two of those didn’t even win their Super Bowls for their original teams.

Projecting quarterback talent poses such a challenge, in part, because of the differences between the college and pro games. Yes, college prospects play the same position. But transitions rarely prove seamless because college offenses and the defenses they face differ so greatly from those on a professional level.

“Just because you’re good at algebra, does that mean you’re good at calculus?” Miami Dolphins coach Mike McDaniel asked. “College football is a different game that has overlapping variables, but it’d be far-fetched to say at any position collegiate success dictates professional success.

“It’s a different orchestration of an 11-person game, and there’s different nuances to it. … And that’s why it’s an inexact science, because the success of the quarterback in the collegiate platform is based upon compounding variables that you have to sift through.”

College and professional football are played with different spacing thanks to different hash mark alignments. In college, the chalk markings that indicate where the ball will be snapped are 40 feet apart. In the NFL, that distance is just 18 feet, 6 inches, meaning the ball is nearer the center of the gridiron. It’s easier to scheme receivers open in college while NFL passing windows are much tighter thanks to narrower hash marks and faster defenders. All of that requires faster decision-making and greater accuracy.

“We’ve got draft picks at every position,” said Jacksonville Jaguars head coach Doug Pederson, a former NFL quarterback himself. “So space and time — the speed element — plays a big factor.”

It takes time for rookies at any position to adjust to that faster-paced game. Then throw in the responsibilities heaped on a quarterback: running a huddle, maybe for the first time (most colleges utilize no-huddle offenses), reading more complex defensive coverages and making protection and play-call changes at the line of scrimmage.

“The amount of information he has to process in such a limited amount of time, all the leadership stuff and intangible stuff … there’s a certain amount of athletic gifts and talent you have to have, and then there’s so much more beyond that,” said Green Bay Packers GM Brian Gutekunst. “And I think the thing that is lost at times is how much of it has to be developed over time.”

NFL coaches and scouts must use their imaginations while studying a prospect to grasp his ability to make that leap.

“You turn on the (college) tape and you’re seeing certain successful, explosive offensive teams that are putting up a ton of points,” Minnesota Vikings coach Kevin O’Connell said. “But you’re trying to figure out if This play right here; this third-down play — does this translate to what we’re going to ask him to do in our offense? … You try to find the traits first, and then go back and apply those traits to (prospects) doing certain things that maybe they don’t even know they’re doing at the time.”

Of course, there are quarterbacks who excel at executing elements of the pro game despite never having displayed such an ability in college. C.J. Stroud , 2023 Offensive Rookie of the Year, played at a top-five level his first year while executing a Houston Texans offense whose concepts and responsibilities differed vastly from the system he ran at Ohio State.

The speed, elusiveness and athleticism Lamar Jackson displayed at Louisville made him a lock to terrorize NFL defenses as a runner, but Jackson faced doubts about his passing ability because many NFL scouts didn’t see many NFL-type throws during evaluations. In six pro seasons with the Baltimore Ravens , Jackson has twice won MVP — including after last season — while being among the league leaders in passer rating.

“We’re only getting to see who a player is right now in college at 21, 22 years old. … It’s a complete projection on who can be better,” Hall of Fame quarterback Kurt Warner said. “We’ve all seen guys who were average college quarterbacks — and you could probably include myself — that become better and great at the next level. … And we’ve all seen other quarterbacks who were great college quarterbacks that never got any better and disappeared in the NFL.

“We throw out, ‘Well, man, their ceiling is so high when they put it all together.’ But we don’t really know what somebody’s ceiling is until they get there or they get close to it. … I also believe that the ceiling for guys is more mental than it is physical, and that’s really hard to truly test.”

Not that NFL teams don’t try, investing in evaluation tools like the Wonderlic and S2 cognition tests in hopes of grasping a quarterback’s intelligence or processing speed. Stroud called into question the S2 test’s credibility last season after his low score leaked and caused some NFL analysts to question his ability to succeed as a pro. The unfazed Texans took Stroud second overall, and he led Houston to the AFC South title after throwing 23 touchdown passes and just five interceptions.

What did NFL learn about S2 after CJ Stroud? 'People in our league can't help themselves'

And that’s why some coaches insist cognitive tests can’t reflect competency the same way that on-field execution and face-to-face X’s and O’s discussions do.

“I know those are all good metrics to go off of — and how players learn and when they process and all that,” Pederson said. “But to me, the bottom line is you’ve got to go spend time with these guys.”

That’s no guarantee, either.

Heading into the 2007 draft, JaMarcus Russell had IT . The 6-foot-6, 256-pound LSU star dwarfed most quarterbacks, even some linemen. And he had a winning pedigree, leading the Tigers to top-10 finishes in his sophomore and junior seasons.

He dazzled during his pro day in front of the 100-plus NFL talent evaluators in Baton Rouge, with the football exploding out of his hand. His 40-yard dash time (4.84 seconds) topped some players a fraction of his size. Some NFL analysts described Russell’s pro day as the best they had ever seen. The Oakland Raiders fell in love and took him first overall three months later.

Three years later, Russell was out of the NFL, never to return.

Blinded by Russell’s marvelous physical gifts, the Raiders had missed red flags. A lengthy contract holdout retarded his development process, and a poor work ethic and problems with substance abuse ensured him a label as one of biggest busts in NFL draft history — and a cautionary tale about the dangers of falling in love with a player based on his pro day performance.

But Russell wasn’t the first player to inflate his draft status with a wondrous workout. Nor was he the last.

Illinois’ Jeff George shined at his 1990 pro day, convincing the Indianapolis Colts to draft him first overall and award him a $15 million contract, an astounding figure for that day. Strong-armed but erratic and undisciplined, George ultimately developed into a journeyman with a 37.1 winning percentage as a starter and 1-2 playoff record.

Zach Wilson ’s performance at his pro day in 2021 sparked comparisons to Aaron Rodgers , and the New York Jets took the former BYU star second overall. But Wilson proved unable to adjust to the massive leap in the level of competition, and a poor developmental plan around him ultimately doomed his prospects. The Jets this week essentially gave him to the Denver Broncos for a swap of late-round picks.

Pro day workouts can certainly inspire NFL decision-makers, but they emphasize physical gifts and not poise, focus, grit and determination — the traits that define great quarterbacks. Mike Tyson once famously said, “Everyone has a plan until they get punched in the mouth.” The same applies to NFL quarterbacks, just replace the punch with a 6-foot-7, 280-pound pass-rusher with track-star speed. Or an All-Pro safety with better ball skills than most college wide receivers. Or a coach or teammate berating them on the sideline for making a mistake.

Great quarterbacks elevate in times of apparent crisis.

“The franchise quarterback is someone you can win because of, rather than just with,” said longtime NFL agent Leigh Steinberg, whose client list includes Patrick Mahomes , Troy Aikman, Steve Young and Warren Moon. “The critical distinction is the quarterback who can have thrown a couple interceptions — the crowd is booing, the game’s getting out of hand, what does he do now?

“A franchise quarterback is someone who can compartmentalize, adopt a quiet mind, filter out past mistakes and elevate his level of play in critical circumstances to take a team to and through victory.”

Gauging a quarterback’s ability to handle adversity is hard, NFL coaches say, because few prospects — most of them the brightest and best athletes on their teams since grade school — have encountered anything in life like the pressure and defeats they will experience in the NFL.

“Some cases, you never really know until you have that time to try to develop a guy,” Gutekunst said.

Steinberg points to the differences in mental makeup that wound up defining the careers of Peyton Manning and Ryan Leaf. In 1998, Steinberg recruited and signed Leaf because the Washington State star possessed greater physical gifts and more impressive measurables than Tennessee’s Manning, who went No. 1 to the Indianapolis Colts.

“Ryan Leaf, physically, was the better quarterback than Peyton Manning,” Steinberg said. “But Peyton Manning had the killer mentality and attention to detail, so Ryan missed that.”

After being selected second by the San Diego Chargers, Leaf played portions of only three NFL seasons, went 4-17 as a starter and threw just 14 touchdown passes against 36 interceptions while getting sacked 65 times.

“In adversity, Ryan Leaf receded and became more isolated and felt embarrassed,” Steinberg said. “He started out very fast in training camp and first couple preseason games, but when he hit a (regular-season) game, then he went into a shell, and the best in terms of therapists and everything else couldn’t get him back out.”

NFL coaches and general managers struggle to explain how to identify the intangibles that forge a quarterback for greatness. But a tireless work ethic, an approach to film study that borders on maniacal and obsessed, displays of strong leadership and respect for the game tend to shine through.

“You’ve got to spend time with him and get to know him a bit and try to give them different things and put them in different situations as much as you can,” Kansas City Chiefs coach Andy Reid said at the NFL Scouting Combine. “You do it while in environments like this. You visit him at college campuses or have them come with us. It’s you talking to their coaches and then talking to the players around them.”

Said Washington Commanders general manager Adam Peters, “I’ve always found in scouting that you make the biggest mistakes more so on the person than the actual talent.”

Plenty of disappointing quarterback prospects have received more blame for their failure to blossom than they should have. Some failed because they landed in environments that gave them little chance for success.

Stanford’s Andrew Luck and Baylor’s Robert Griffin III, the first two picks of the 2012 draft, were supposed to transform Indianapolis and Washington into perennial contenders, respectively. But Luck’s poor supporting cast — especially along the Colts’ offensive line — led to his stunning retirement six years later. And dysfunction largely ruined Griffin’s promising career in Washington.

The Colts essentially committed malpractice through negligence. Frugal free agency spending, poor drafts and the repeated refusal to upgrade a weak line placed Luck in ever-present risk of serious injury. By the time his career abruptly concluded, the quarterback’s gauntlet of serious injuries included a high-ankle sprain, calf strain, abdominal muscle tear, lacerated kidney, concussion and torn labrum in his shoulder.

Meanwhile, Griffin landed with a franchise mired in turmoil. Then-owner Daniel Snyder undermined then-coach Mike Shanahan and poisoned the relationship between quarterback and coach. Griffin managed to put up a historic rookie season before suffering a knee injury that required reconstructive surgery. Then, desperate to save his job, Shanahan allowed Griffin to rush back in 2013. Griffin regressed while backup Kirk Cousins ascended, causing a quarterback controversy. Shanahan was fired at the end of the season. Griffin never recaptured his magic.

How does the greatest QB prospect since Elway walk away before he’s 30? Inside 'Luck'

Organizational alignment, a clear development plan and quality teammates are among the must-haves for quarterback success. New England discovered Brady and developed him into a future Hall of Famer who delivered six Lombardi Trophies and 20 years of stability. Kansas City is the league’s latest dynasty with Mahomes as the centerpiece thanks to similar top-to-bottom alignment and a carefully planned quarterback development plan.

“There’s a lot of things that go into that,” Reid said. “Where’s the staff at? Are they in their last year, are they in their first year? What offense are you asking this guy to be in? Will it work with his strengths and will you try to better the things that he’s not as strong at, weaknesses? Are you willing to stand before (reporters) and kind of protect that guy so he can grow a little bit?”

In each case when he groomed a top young quarterback, Reid exercised patience. In Philadelphia, Donovan McNabb backed up Pederson after being selected second overall in 1999, playing only sparingly until Week 10, when Reid felt comfortable enough to turn the reins over. Perhaps it’s no coincidence that of the four quarterbacks picked in the first round that year, only McNabb and Minnesota’s Daunte Culpepper, who also sat initially, became effective starters. Draft mates Tim Couch and Akili Smith, who played before they were ready, were deemed busts.

In Kansas City, Mahomes sat behind Alex Smith on a playoff team for a year before becoming the starter. Three Super Bowls and two MVP honors later …

Steinberg believes public displays of patience and sincere acts of support are crucial to a quarterback’s development. He pointed to the friction and public arguments between Leaf and then- Chargers general manager Bobby Beathard during the quarterback’s bad stretches of play and noted the contrast between then-Colts GM Bill Polian’s repeated proclamations of support for Manning despite his leading the NFL in interceptions as a rookie.

“It is a combination of scouting and development skills,” Steinberg said. “All the quarterbacks that haven’t made it, they can’t all have been bad draft picks. They just can’t. Drafting and the evaluation process is way too thorough.”

Understanding the need for time and development at the position, the Packers have repeatedly drafted quarterbacks before their starter’s play drops off, so coaches don’t feel the pressure to rush a young prospect on the field prematurely. As a result, Green Bay has had relatively clear succession plans from Brett Favre to Rodgers to Jordan Love , leading to stability for three decades.

But the Packers are the outliers. Troubled organizations are most often the ones in need of franchise savior. The desperation for transformational talent — both to contend and to save jobs — clouds judgment and leads to mind-blowing blunders, sometimes when warning signs are right there.

In 2021, Zach Wilson went to the Jets second overall with no clear development plan despite the leap in competition level. North Dakota State’s Trey Lance was a project player who San Francisco punted on just two seasons after giving up three first-round picks to move up to No. 3 to select him. Ohio State’s Justin Fields , Chicago’s pick at No. 11, played behind horrendous lines and for coaches and coordinators who never displayed a firm grasp of his strengths and weaknesses. And Mac Jones went 15th to quarterback-needy New England when most rival scouts had a third-round grade on him.

After Wilson’s trade to Denver this week, none of the four is on the roster of the team that drafted him.

The Bears have used 36 different starting quarterbacks since Favre first suited up for Green Bay. In 2017, they traded up to draft Mitch Trubisky (now a journeyman backup) over Mahomes. Five years later, they drafted Fields. Now, after moving on from Fields this offseason, Chicago is expected to take Caleb Williams first overall with hopes that this time things will be different.

• NFL Draft 2024 ‘The Beast’ Guide: Dane Brugler’s scouting reports and player rankings
• 2024 NFL Draft rankings: Dane Brugler breaks down the top 300 prospects
• A beginner’s guide to the 2024 NFL Draft: No. 1 pick, trade rumors, full order and more

(Illustration: Eamonn Dalton / The Athletic ; photos: Jordon Kelly, Kevin Sabitus / Getty Images)

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Mike Jones joined The Athletic as a national NFL writer in 2022 after five years at USA Today, where he covered the NFL, and eight years at The Washington Post, where he covered the Washington Commanders. He previously covered the Washington Wizards for The Washington Times. Mike is a native of Warrenton, Va.

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Americans continue to have doubts about climate scientists’ understanding of climate change

Only about one-third of Americans think climate scientists understand very well whether climate change is happening, according to a new Pew Research Center survey . And only about a quarter or less say climate scientists understand very well the effect climate change has on extreme weather, its causes and the best ways to address it.

Pew Research Center conducted this study to understand Americans’ views of how much climate scientists understand key aspects of climate change, and their role in policy debates on this topic. Read the accompanying report for more on Americans’ views of future harms from climate change.

This survey was conducted among 8,842 U.S. adults from Sept. 25 to Oct. 1, 2023. Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this analysis , along with responses, and its methodology .

Americans rate climate scientists’ understanding of aspects of climate change slightly lower than they did two years ago and the same or lower than in 2016.

The share of Americans who say climate scientists understand very well whether climate change is occurring decreased from 37% in 2021 to 32% this year.

Similarly, the share of Americans who say climate scientists understand the causes of climate change very well decreased slightly from 28% in 2021 to 24% today. And only 13% of Americans now say climate scientists understand very well the best ways to address climate change, down from 18% in 2021.

Analysis of recent scientific publications finds widespread agreement among climate scientists that human activity is the primary cause of climate change. The Center recently conducted in-depth interviews to better understand the views of adults who say climate change is not an urgent issue and are unconvinced human activity is its main cause.

Partisan differences in views of climate scientists

Democrats continue to rate climate scientists’ understanding much higher than Republicans do.

When asked how well climate scientists understand whether climate change is happening, 52% of Democrats and Democratic-leaning independents say climate scientists understand this very well . In comparison, 51% of Republicans and Republican leaners say climate scientists understand this not too or not at all well .

Democrats are also four times as likely as Republicans to say climate scientists understand very well how climate change affects extreme weather events (40% vs. 10%). Scientific studies have found that extreme weather events will become more frequent and intense with climate change .

When it comes to the causes of climate change, 41% of Democrats say climate scientists understand this very well, compared with 7% of Republicans. About six-in-ten Republicans (59%) say climate scientists understand this not too or at all well.

Small shares of both Democrats and Republicans say climate scientists understand very well the best ways to address climate change, though Democrats are more likely to say this (23% vs. 4%, respectively). Republicans are far more likely than Democrats to say climate scientists understand this not too or at all well (71% vs. 24%).

Differences by education level and party

Democrats with more education rate climate scientists’ understanding higher than Democrats with less education. But how Republicans rate scientists’ understanding of aspects of climate change does not differ by education level. For example:

• 72% of Democrats with a postgraduate degree say climate scientists understand very well whether climate change is occurring. In comparison, 36% of Democrats with a high school degree or less say this – a 36 percentage point difference.
• Small shares of Republicans across education levels think climate scientists understand very well whether climate change is happening: 13% of Republicans with a postgraduate degree say this, as do 10% of Republicans with a high school degree or less.

These patterns also hold when Democrats and Republicans are asked about climate scientists’ understanding of the causes of climate change and its effect on extreme weather.

Past Center surveys have found that views about the role of human activity also vary by education level among Democrats but not Republicans.

Views of climate scientists’ influence on policy

When asked about climate scientists’ role in policy debates about climate change, half of Americans say they have too little influence. This share is down 4 points from 2021.

Smaller shares say climate scientists have too much (26%) or about the right amount of influence (22%) in policy debates.

In keeping with the wide partisan differences in ratings of climate scientists’ understanding of aspects of climate change, Democrats and Republicans are deeply divided about the appropriate role of climate scientists in policy debates.

Three-quarters of Democrats say climate scientists have too little influence in policy debates about climate change, while one-quarter of Republicans say the same – a difference of 50 points. About half of Republicans (49%) think climate scientists have too much influence.

There are also ideological divides within the GOP on climate scientists’ policy influence. Conservative Republicans are about twice as likely as moderate and liberal Republicans to say climate scientists have too much influence in public policy debates (60% vs. 29%).

Note: Here are the questions used for this analysis , along with responses, and its methodology .

• Climate, Energy & Environment
• Partisanship & Issues
• Trust in Science

Giancarlo Pasquini is a research associate focusing on science and society research at Pew Research Center

Brian Kennedy is a senior researcher focusing on science and society research at Pew Research Center

How Republicans view climate change and energy issues

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Engineering student studying flight physics of birds

Sameer pokhrel is working towards advancement in unmanned aerial vehicles.

After earning a bachelor's degree in mechanical engineering in Nepal, Sameer Pokhrel came to the United States to further his education. From an early age, he had a lifelong fascination with aviation. As an adult, he transformed this fascination into a career, pursuing a doctoral degree in aerospace engineering at the University of Cincinnati's historic program. Here, he has succeeded in research, instruction, and was named Graduate Student Engineer of the Month by the College of Engineering and Applied Science.

Why did you choose UC? What drew you here?

Sameer Pokhrel is a doctoral candidate in aerospace engineering at the University of Cincinnati. Photo/provided

I chose the University of Cincinnati primarily because of its strong reputation in aerospace engineering and research.

From an early age, I was fascinated by airplanes and rockets. UC's esteemed reputation in the field of aerospace engineering made me feel like it was the perfect place for my graduate studies. Even though I didn't have the opportunity to visit campus before applying, hearing positive feedback about the university's facilities, resources, and faculty helped my decision.

UC offers the ideal environment for me to grow academically and is preparing me to thrive in my field. I'm glad I chose to be a Bearcat!

Why did you choose your field of study?

When I was young, I would often go plane spotting whenever possible. I remember I used to get very excited when I saw space exploration documentaries on TV.

Later, I realized I could turn this fascination into a career, so I chose mechanical engineering for my undergraduate degree. As aerospace engineering was not directly available at the time in Nepal, I chose it as my minor.

After completing my undergraduate studies, I worked as a design engineer on a fixed wing Unmanned Aerial Vehicle (UAV) for medical delivery in the hilly region of Nepal. There, I realized my interest in dynamics and control, which led me to pursue a graduate degree in aerospace engineering, focusing on dynamics and control. 

Describe your research work. Why does it inspire you?

In my research, I focus on studying the application of unconventional control techniques in bio-inspired systems of UAVs. My work can be divided into two main parts: theoretical developments and applications. On the theoretical front, I work nonlinear control techniques, particularly Extremum Seeking Control, which is a model-free, adaptive control technique. I aim to develop tools to better analyze and improve the structures of such control systems for real-life applications. On the application front, I explore the flight physics of soaring birds, which fly long distances without flapping their wings. I investigate whether we can mimic the optimized flight of these birds in UAVs by examining the relationship between extremum seeking control and their flight patterns. 

What inspires me most about this research is the opportunity to push the boundaries of current literature and bridge the gap between theory and practice.

I'm driven by the prospect of developing novel control techniques that are versatile and less dependent on specific models. Furthermore, if we can replicate the dynamic soaring flight maneuver of birds, it could lead to substantial technological advancements in UAVs. Imagine the possibility of flying UAVs for hundreds of kilometers like soaring birds.

This perspective is truly miraculous and motivates me to continue exploring and innovating in this field. 

What are a few accomplishments of which you are most proud?

Academically, I'm proud to have published my research work in prestigious journals such as the SIAM Journal on Applied Mathematics, the International Journal of Control, Automation and Systems, and Bioinspiration and Biomimetics.

I believe these publications have not only validated my research efforts but have also contributed to the academic community. Moreover, presenting my research at conferences like the American Institute of Aeronautics and Astronautics SciTech, the Society for Industrial and Applied Mathematics (SIAM) Conference of Control and its Applications, and the SIAM Conference on Life Science was immensely beneficial. 

These experiences allowed me to share my work with peers and experts while simultaneously providing me with valuable learning and networking opportunities.

Additionally, participating in events like the Graduate Student Mathematical Modeling Camp and the Mathematical Problems in Industry Workshop 2023 helped me experience practical industry problems. The time I spent with bright minds during the brainstorming sessions is something I will never forget.

Also, I'd like to give a huge shoutout to the UC Piloting Club for providing me with a real flying experience by putting me in the co-pilot seat of a real airplane. All of these experiences have been instrumental and impactful in shaping my academic and personal journey during my time at the university. 

When do you expect to graduate? Do you have any other activities you'd like to share?

I expect to graduate in the summer of 2024 and hope to get experience in industry before returning to academia. I also love to travel and experience new things. Traveling provides the necessary break between projects and reenergizes me for my upcoming work. I also love watching and playing sports, especially soccer, which I play on a regular basis. 

Want to learn more?

Explore graduate programs at the College of Engineering and Applied Science. 

Featured image at top: UAV flying. Photo/pixabay

• College of Engineering and Applied Science
• Student Experience
• Aerospace Engineering and Engineering Mechanics

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