essay questions for statistics

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There are lies, damned lies, and statistics. —Mark Twain

What this handout is about

The purpose of this handout is to help you use statistics to make your argument as effectively as possible.

Introduction

Numbers are power. Apparently freed of all the squishiness and ambiguity of words, numbers and statistics are powerful pieces of evidence that can effectively strengthen any argument. But statistics are not a panacea. As simple and straightforward as these little numbers promise to be, statistics, if not used carefully, can create more problems than they solve.

Many writers lack a firm grasp of the statistics they are using. The average reader does not know how to properly evaluate and interpret the statistics he or she reads. The main reason behind the poor use of statistics is a lack of understanding about what statistics can and cannot do. Many people think that statistics can speak for themselves. But numbers are as ambiguous as words and need just as much explanation.

In many ways, this problem is quite similar to that experienced with direct quotes. Too often, quotes are expected to do all the work and are treated as part of the argument, rather than a piece of evidence requiring interpretation (see our handout on how to quote .) But if you leave the interpretation up to the reader, who knows what sort of off-the-wall interpretations may result? The only way to avoid this danger is to supply the interpretation yourself.

But before we start writing statistics, let’s actually read a few.

Reading statistics

As stated before, numbers are powerful. This is one of the reasons why statistics can be such persuasive pieces of evidence. However, this same power can also make numbers and statistics intimidating. That is, we too often accept them as gospel, without ever questioning their veracity or appropriateness. While this may seem like a positive trait when you plug them into your paper and pray for your reader to submit to their power, remember that before we are writers of statistics, we are readers. And to be effective readers means asking the hard questions. Below you will find a useful set of hard questions to ask of the numbers you find.

1. Does your evidence come from reliable sources?

This is an important question not only with statistics, but with any evidence you use in your papers. As we will see in this handout, there are many ways statistics can be played with and misrepresented in order to produce a desired outcome. Therefore, you want to take your statistics from reliable sources (for more information on finding reliable sources, please see our handout on evaluating print sources ). This is not to say that reliable sources are infallible, but only that they are probably less likely to use deceptive practices. With a credible source, you may not need to worry as much about the questions that follow. Still, remember that reading statistics is a bit like being in the middle of a war: trust no one; suspect everyone.

2. What is the data’s background?

Data and statistics do not just fall from heaven fully formed. They are always the product of research. Therefore, to understand the statistics, you should also know where they come from. For example, if the statistics come from a survey or poll, some questions to ask include:

• Who asked the questions in the survey/poll?
• What, exactly, were the questions?
• Who interpreted the data?
• What issue prompted the survey/poll?
• What (policy/procedure) potentially hinges on the results of the poll?
• Who stands to gain from particular interpretations of the data?

All these questions help you orient yourself toward possible biases or weaknesses in the data you are reading. The goal of this exercise is not to find “pure, objective” data but to make any biases explicit, in order to more accurately interpret the evidence.

3. Are all data reported?

In most cases, the answer to this question is easy: no, they aren’t. Therefore, a better way to think about this issue is to ask whether all data have been presented in context. But it is much more complicated when you consider the bigger issue, which is whether the text or source presents enough evidence for you to draw your own conclusion. A reliable source should not exclude data that contradicts or weakens the information presented.

An example can be found on the evening news. If you think about ice storms, which make life so difficult in the winter, you will certainly remember the newscasters warning people to stay off the roads because they are so treacherous. To verify this point, they tell you that the Highway Patrol has already reported 25 accidents during the day. Their intention is to scare you into staying home with this number. While this number sounds high, some studies have found that the number of accidents actually goes down on days with severe weather. Why is that? One possible explanation is that with fewer people on the road, even with the dangerous conditions, the number of accidents will be less than on an “average” day. The critical lesson here is that even when the general interpretation is “accurate,” the data may not actually be evidence for the particular interpretation. This means you have no way to verify if the interpretation is in fact correct.

There is generally a comparison implied in the use of statistics. How can you make a valid comparison without having all the facts? Good question. You may have to look to another source or sources to find all the data you need.

4. Have the data been interpreted correctly?

If the author gives you her statistics, it is always wise to interpret them yourself. That is, while it is useful to read and understand the author’s interpretation, it is merely that—an interpretation. It is not the final word on the matter. Furthermore, sometimes authors (including you, so be careful) can use perfectly good statistics and come up with perfectly bad interpretations. Here are two common mistakes to watch out for:

• Confusing correlation with causation. Just because two things vary together does not mean that one of them is causing the other. It could be nothing more than a coincidence, or both could be caused by a third factor. Such a relationship is called spurious.The classic example is a study that found that the more firefighters sent to put out a fire, the more damage the fire did. Yikes! I thought firefighters were supposed to make things better, not worse! But before we start shutting down fire stations, it might be useful to entertain alternative explanations. This seemingly contradictory finding can be easily explained by pointing to a third factor that causes both: the size of the fire. The lesson here? Correlation does not equal causation. So it is important not only to think about showing that two variables co-vary, but also about the causal mechanism.
• Ignoring the margin of error. When survey results are reported, they frequently include a margin of error. You might see this written as “a margin of error of plus or minus 5 percentage points.” What does this mean? The simple story is that surveys are normally generated from samples of a larger population, and thus they are never exact. There is always a confidence interval within which the general population is expected to fall. Thus, if I say that the number of UNC students who find it difficult to use statistics in their writing is 60%, plus or minus 4%, that means, assuming the normal confidence interval of 95%, that with 95% certainty we can say that the actual number is between 56% and 64%.

Why does this matter? Because if after introducing this handout to the students of UNC, a new poll finds that only 56%, plus or minus 3%, are having difficulty with statistics, I could go to the Writing Center director and ask for a raise, since I have made a significant contribution to the writing skills of the students on campus. However, she would no doubt point out that a) this may be a spurious relationship (see above) and b) the actual change is not significant because it falls within the margin of error for the original results. The lesson here? Margins of error matter, so you cannot just compare simple percentages.

Finally, you should keep in mind that the source you are actually looking at may not be the original source of your data. That is, if you find an essay that quotes a number of statistics in support of its argument, often the author of the essay is using someone else’s data. Thus, you need to consider not only your source, but the author’s sources as well.

Writing statistics

As you write with statistics, remember your own experience as a reader of statistics. Don’t forget how frustrated you were when you came across unclear statistics and how thankful you were to read well-presented ones. It is a sign of respect to your reader to be as clear and straightforward as you can be with your numbers. Nobody likes to be played for a fool. Thus, even if you think that changing the numbers just a little bit will help your argument, do not give in to the temptation.

As you begin writing, keep the following in mind. First, your reader will want to know the answers to the same questions that we discussed above. Second, you want to present your statistics in a clear, unambiguous manner. Below you will find a list of some common pitfalls in the world of statistics, along with suggestions for avoiding them.

1. The mistake of the “average” writer

Nobody wants to be average. Moreover, nobody wants to just see the word “average” in a piece of writing. Why? Because nobody knows exactly what it means. There are not one, not two, but three different definitions of “average” in statistics, and when you use the word, your reader has only a 33.3% chance of guessing correctly which one you mean.

For the following definitions, please refer to this set of numbers: 5, 5, 5, 8, 12, 14, 21, 33, 38

• Mean (arithmetic mean) This may be the most average definition of average (whatever that means). This is the weighted average—a total of all numbers included divided by the quantity of numbers represented. Thus the mean of the above set of numbers is 5+5+5+8+12+14+21+33+38, all divided by 9, which equals 15.644444444444 (Wow! That is a lot of numbers after the decimal—what do we do about that? Precision is a good thing, but too much of it is over the top; it does not necessarily make your argument any stronger. Consider the reasonable amount of precision based on your input and round accordingly. In this case, 15.6 should do the trick.)
• Median Depending on whether you have an odd or even set of numbers, the median is either a) the number midway through an odd set of numbers or b) a value halfway between the two middle numbers in an even set. For the above set (an odd set of 9 numbers), the median is 12. (5, 5, 5, 8 < 12 < 14, 21, 33, 38)
• Mode The mode is the number or value that occurs most frequently in a series. If, by some cruel twist of fate, two or more values occur with the same frequency, then you take the mean of the values. For our set, the mode would be 5, since it occurs 3 times, whereas all other numbers occur only once.

As you can see, the numbers can vary considerably, as can their significance. Therefore, the writer should always inform the reader which average he or she is using. Otherwise, confusion will inevitably ensue.

2. Match your facts with your questions

Be sure that your statistics actually apply to the point/argument you are making. If we return to our discussion of averages, depending on the question you are interesting in answering, you should use the proper statistics.

Perhaps an example would help illustrate this point. Your professor hands back the midterm. The grades are distributed as follows:

The professor felt that the test must have been too easy, because the average (median) grade was a 95.

When a colleague asked her about how the midterm grades came out, she answered, knowing that her classes were gaining a reputation for being “too easy,” that the average (mean) grade was an 80.

When your parents ask you how you can justify doing so poorly on the midterm, you answer, “Don’t worry about my 63. It is not as bad as it sounds. The average (mode) grade was a 58.”

I will leave it up to you to decide whether these choices are appropriate. Selecting the appropriate facts or statistics will help your argument immensely. Not only will they actually support your point, but they will not undermine the legitimacy of your position. Think about how your parents will react when they learn from the professor that the average (median) grade was 95! The best way to maintain precision is to specify which of the three forms of “average” you are using.

3. Show the entire picture

Sometimes, you may misrepresent your evidence by accident and misunderstanding. Other times, however, misrepresentation may be slightly less innocent. This can be seen most readily in visual aids. Do not shape and “massage” the representation so that it “best supports” your argument. This can be achieved by presenting charts/graphs in numerous different ways. Either the range can be shortened (to cut out data points which do not fit, e.g., starting a time series too late or ending it too soon), or the scale can be manipulated so that small changes look big and vice versa. Furthermore, do not fiddle with the proportions, either vertically or horizontally. The fact that USA Today seems to get away with these techniques does not make them OK for an academic argument.

Charts A, B, and C all use the same data points, but the stories they seem to be telling are quite different. Chart A shows a mild increase, followed by a slow decline. Chart B, on the other hand, reveals a steep jump, with a sharp drop-off immediately following. Conversely, Chart C seems to demonstrate that there was virtually no change over time. These variations are a product of changing the scale of the chart. One way to alleviate this problem is to supplement the chart by using the actual numbers in your text, in the spirit of full disclosure.

Another point of concern can be seen in Charts D and E. Both use the same data as charts A, B, and C for the years 1985-2000, but additional time points, using two hypothetical sets of data, have been added back to 1965. Given the different trends leading up to 1985, consider how the significance of recent events can change. In Chart D, the downward trend from 1990 to 2000 is going against a long-term upward trend, whereas in Chart E, it is merely the continuation of a larger downward trend after a brief upward turn.

One of the difficulties with visual aids is that there is no hard and fast rule about how much to include and what to exclude. Judgment is always involved. In general, be sure to present your visual aids so that your readers can draw their own conclusions from the facts and verify your assertions. If what you have cut out could affect the reader’s interpretation of your data, then you might consider keeping it.

4. Give bases of all percentages

Because percentages are always derived from a specific base, they are meaningless until associated with a base. So even if I tell you that after this reading this handout, you will be 23% more persuasive as a writer, that is not a very meaningful assertion because you have no idea what it is based on—23% more persuasive than what?

Let’s look at crime rates to see how this works. Suppose we have two cities, Springfield and Shelbyville. In Springfield, the murder rate has gone up 75%, while in Shelbyville, the rate has only increased by 10%. Which city is having a bigger murder problem? Well, that’s obvious, right? It has to be Springfield. After all, 75% is bigger than 10%.

Hold on a second, because this is actually much less clear than it looks. In order to really know which city has a worse problem, we have to look at the actual numbers. If I told you that Springfield had 4 murders last year and 7 this year, and Shelbyville had 30 murders last year and 33 murders this year, would you change your answer? Maybe, since 33 murders are significantly more than 7. One would certainly feel safer in Springfield, right?

Not so fast, because we still do not have all the facts. We have to make the comparison between the two based on equivalent standards. To do that, we have to look at the per capita rate (often given in rates per 100,000 people per year). If Springfield has 700 residents while Shelbyville has 3.3 million, then Springfield has a murder rate of 1,000 per 100,000 people, and Shelbyville’s rate is merely 1 per 100,000. Gadzooks! The residents of Springfield are dropping like flies. I think I’ll stick with nice, safe Shelbyville, thank you very much.

Percentages are really no different from any other form of statistics: they gain their meaning only through their context. Consequently, percentages should be presented in context so that readers can draw their own conclusions as you emphasize facts important to your argument. Remember, if your statistics really do support your point, then you should have no fear of revealing the larger context that frames them.

Important questions to ask (and answer) about statistics

• Is the question being asked relevant?
• Do the data come from reliable sources?
• Margin of error/confidence interval—when is a change really a change?
• Are all data reported, or just the best/worst?
• Are the data presented in context?
• Have the data been interpreted correctly?
• Does the author confuse correlation with causation?

Now that you have learned the lessons of statistics, you have two options. Use this knowledge to manipulate your numbers to your advantage, or use this knowledge to better understand and use statistics to make accurate and fair arguments. The choice is yours. Nine out of ten writers, however, prefer the latter, and the other one later regrets his or her decision.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Statistics - List of Free Essay Examples And Topic Ideas

Statistics, as the science of collecting, analyzing, and interpreting data, plays an indispensable role in modern decision-making and knowledge generation. Essays could explore the myriad applications of statistics across various fields including healthcare, economics, and social sciences. They might delve into key statistical concepts, methods, and tools, illustrating how they help in understanding complex phenomena, making predictions, and informing policy. Discussions might also extend to the ethical considerations inherent in statistical practices, such as data integrity, privacy, and the potential for misrepresentation or bias. The discourse may also touch on the evolving landscape of statistics amid the advent of big data and computational advancements, examining how these developments are expanding the capabilities and applications of statistical analysis. We have collected a large number of free essay examples about Statistics you can find at PapersOwl Website. You can use our samples for inspiration to write your own essay, research paper, or just to explore a new topic for yourself.

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How to Write a Statistics Essay: Short Guide

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Writing a statistics essay involves more than just presenting numbers and data. It requires a clear understanding of statistical methods, an ability to interpret results, and the skill to communicate findings effectively. This article provides a step-by-step guide on how to write a compelling statistics essay.

Understanding the Essay Question

Firstly, it's essential to comprehend the specific question or topic you are dealing with. A statistics essay could range from analyzing a set of data to discussing a particular statistical method. Understanding the scope, requirements, and objectives of the essay will guide your research and writing process.

Research and Data Collection

Begin by collecting relevant data for your essay. This could involve gathering existing data or conducting your own research. Ensure that your sources are credible and that your data is accurate. Additionally, familiarize yourself with the statistical methods that are appropriate for analyzing your data.

Planning Your Essay

Organize your thoughts and data before you start writing. This includes outlining the structure of your essay and deciding how you will present your data. A typical structure might include an introduction, a methodology section, a data analysis section, and a conclusion.

Writing the Introduction

Your introduction should set the context for your essay. Explain why the topic is important and how your essay addresses it. Introduce your thesis statement or the main argument of your essay.

Methodology

In this section, describe the methods used to collect and analyze your data. Be detailed so that readers understand how you arrived at your conclusions. This might include discussing sample sizes, variables, and statistical tests used.

Data Analysis

This is the core of your statistics essay. Present your data in a clear and structured manner. Use graphs, tables, and charts to illustrate your points. Interpret the results of your analysis, explaining what the data shows and why it is significant.

Discussing Results

Go beyond just presenting data. Discuss what the results mean in the context of your topic. Compare your findings with other studies and theories. Address any limitations in your study and suggest areas for further research.

Summarize the main points of your essay, restating your thesis in light of the evidence presented. Highlight the significance of your findings and how they contribute to the understanding of the topic.

Referencing and Citation

Accurately cite all the sources and data used in your essay. Follow the required citation style (APA, MLA, Chicago, etc.). Proper citation is essential to avoid plagiarism and to give credit to the original authors.

Proofreading and Editing

Finally, revise your essay. Check for grammatical and spelling errors, ensure clarity and flow, and verify that all data is accurately presented. Peer reviews can be helpful in identifying areas for improvement.

In conclusion, writing a statistics essay requires careful planning, thorough research, and clear presentation of data and findings. By following these guidelines, you can effectively communicate complex statistical information and insights, contributing meaningfully to the topic of discussion.

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Introductory essay

Written by the educators who created Visualizing Data, a brief look at the key facts, tough questions and big ideas in their field. Begin this TED Study with a fascinating read that gives context and clarity to the material.

The reality of today

All of us now are being blasted by information design. It's being poured into our eyes through the Web, and we're all visualizers now; we're all demanding a visual aspect to our information...And if you're navigating a dense information jungle, coming across a beautiful graphic or a lovely data visualization, it's a relief, it's like coming across a clearing in the jungle. David McCandless

In today's complex 'information jungle,' David McCandless observes that "Data is the new soil." McCandless, a data journalist and information designer, celebrates data as a ubiquitous resource providing a fertile and creative medium from which new ideas and understanding can grow. McCandless's inspiration, statistician Hans Rosling, builds on this idea in his own TEDTalk with his compelling image of flowers growing out of data/soil. These 'flowers' represent the many insights that can be gleaned from effective visualization of data.

We're just learning how to till this soil and make sense of the mountains of data constantly being generated. As Gary King, Director of Harvard's Institute for Quantitative Social Science says in his New York Times article "The Age of Big Data":

It's a revolution. We're really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government. There is no area that is going to be untouched.

How do we deal with all this data without getting information overload? How do we use data to gain real insight into the world? Finding ways to pull interesting information out of data can be very rewarding, both personally and professionally. The managing editor of Financial Times observed on CNN's Your Money : "The people who are able to in a sophisticated and practical way analyze that data are going to have terrific jobs." Those who learn how to present data in effective ways will be valuable in every field.

Many people, when they think of data, think of tables filled with numbers. But this long-held notion is eroding. Today, we're generating streams of data that are often too complex to be presented in a simple "table." In his TEDTalk, Blaise Aguera y Arcas explores images as data, while Deb Roy uses audio, video, and the text messages in social media as data.

Some may also think that only a few specialized professionals can draw insights from data. When we look at data in the right way, however, the results can be fun, insightful, even whimsical — and accessible to everyone! Who knew, for example, that there are more relationship break-ups on Monday than on any other day of the week, or that the most break-ups (at least those discussed on Facebook) occur in mid-December? David McCandless discovered this by analyzing thousands of Facebook status updates.

Data, data, everywhere

There is more data available to us now than we can possibly process. Every minute , Internet users add the following to the big data pool (i):

• 204,166,667 email messages sent
• More than 2,000,000 Google searches
• 684,478 pieces of content added on Facebook
• $272,070 spent by consumers via online shopping
• More than 100,000 tweets on Twitter
• 47,000 app downloads from Apple
• 34,722 "likes" on Facebook for different brands and organizations
• 27,778 new posts on Tumblr blogs
• 3,600 new photos on Instagram
• 3,125 new photos on Flickr
• 2,083 check-ins on Foursquare
• 571 new websites created
• 347 new blog posts published on Wordpress
• 217 new mobile web users
• 48 hours of new video on YouTube

These numbers are almost certainly higher now, as you read this. And this just describes a small piece of the data being generated and stored by humanity. We're all leaving data trails — not just on the Internet, but in everything we do. This includes reams of financial data (from credit cards, businesses, and Wall Street), demographic data on the world's populations, meteorological data on weather and the environment, retail sales data that records everything we buy, nutritional data on food and restaurants, sports data of all types, and so on.

Governments are using data to search for terrorist plots, retailers are using it to maximize marketing strategies, and health organizations are using it to track outbreaks of the flu. But did you ever think of collecting data on every minute of your child's life? That's precisely what Deb Roy did. He recorded 90,000 hours of video and 140,000 hours of audio during his son's first years. That's a lot of data! He and his colleagues are using the data to understand how children learn language, and they're now extending this work to analyze publicly available conversations on social media, allowing them to take "the real-time pulse of a nation."

Data can provide us with new and deeper insight into our world. It can help break stereotypes and build understanding. But the sheer quantity of data, even in just any one small area of interest, is overwhelming. How can we make sense of some of this data in an insightful way?

The power of visualizing data

Visualization can help transform these mountains of data into meaningful information. In his TEDTalk, David McCandless comments that the sense of sight has by far the fastest and biggest bandwidth of any of the five senses. Indeed, about 80% of the information we take in is by eye. Data that seems impenetrable can come alive if presented well in a picture, graph, or even a movie. Hans Rosling tells us that "Students get very excited — and policy-makers and the corporate sector — when they can see the data."

It makes sense that, if we can effectively display data visually, we can make it accessible and understandable to more people. Should we worry, however, that by condensing data into a graph, we are simplifying too much and losing some of the important features of the data? Let's look at a fascinating study conducted by researchers Emre Soyer and Robin Hogarth . The study was conducted on economists, who are certainly no strangers to statistical analysis. Three groups of economists were asked the same question concerning a dataset:

• One group was given the data and a standard statistical analysis of the data; 72% of these economists got the answer wrong.
• Another group was given the data, the statistical analysis, and a graph; still 61% of these economists got the answer wrong.
• A third group was given only the graph, and only 3% got the answer wrong.

Visualizing data can sometimes be less misleading than using the raw numbers and statistics!

What about all the rest of us, who may not be professional economists or statisticians? Nathalie Miebach finds that making art out of data allows people an alternative entry into science. She transforms mountains of weather data into tactile physical structures and musical scores, adding both touch and hearing to the sense of sight to build even greater understanding of data.

Another artist, Chris Jordan, is concerned about our ability to comprehend big numbers. As citizens of an ever-more connected global world, we have an increased need to get useable information from big data — big in terms of the volume of numbers as well as their size. Jordan's art is designed to help us process such numbers, especially numbers that relate to issues of addiction and waste. For example, Jordan notes that the United States has the largest percentage of its population in prison of any country on earth: 2.3 million people in prison in the United States in 2005 and the number continues to rise. Jordan uses art, in this case a super-sized image of 2.3 million prison jumpsuits, to help us see that number and to help us begin to process the societal implications of that single data value. Because our brains can't truly process such a large number, his artwork makes it real.

The role of technology in visualizing data

The TEDTalks in this collection depend to varying degrees on sophisticated technology to gather, store, process, and display data. Handling massive amounts of data (e.g., David McCandless tracking 10,000 changes in Facebook status, Blaise Aguera y Arcas synching thousands of online images of the Notre Dame Cathedral, or Deb Roy searching for individual words in 90,000 hours of video tape) requires cutting-edge computing tools that have been developed specifically to address the challenges of big data. The ability to manipulate color, size, location, motion, and sound to discover and display important features of data in a way that makes it readily accessible to ordinary humans is a challenging task that depends heavily on increasingly sophisticated technology.

The importance of good visualization

There are good ways and bad ways of presenting data. Many examples of outstanding presentations of data are shown in the TEDTalks. However, sometimes visualizations of data can be ineffective or downright misleading. For example, an inappropriate scale might make a relatively small difference look much more substantial than it should be, or an overly complicated display might obfuscate the main relationships in the data. Statistician Kaiser Fung's blog Junk Charts offers many examples of poor representations of data (and some good ones) with descriptions to help the reader understand what makes a graph effective or ineffective. For more examples of both good and bad representations of data, see data visualization architect Andy Kirk's blog at visualisingdata.com . Both consistently have very current examples from up-to-date sources and events.

Creativity, even artistic ability, helps us see data in new ways. Magic happens when interesting data meets effective design: when statistician meets designer (sometimes within the same person). We are fortunate to live in a time when interactive and animated graphs are becoming commonplace, and these tools can be incredibly powerful. Other times, simpler graphs might be more effective. The key is to present data in a way that is visually appealing while allowing the data to speak for itself.

Changing perceptions through data

While graphs and charts can lead to misunderstandings, there is ultimately "truth in numbers." As Steven Levitt and Stephen Dubner say in Freakonomics , "[T]eachers and criminals and real-estate agents may lie, and politicians, and even C.I.A. analysts. But numbers don't." Indeed, consideration of data can often be the easiest way to glean objective insights. Again from Freakonomics : "There is nothing like the sheer power of numbers to scrub away layers of confusion and contradiction."

Data can help us understand the world as it is, not as we believe it to be. As Hans Rosling demonstrates, it's often not ignorance but our preconceived ideas that get in the way of understanding the world as it is. Publicly-available statistics can reshape our world view: Rosling encourages us to "let the dataset change your mindset."

Chris Jordan's powerful images of waste and addiction make us face, rather than deny, the facts. It's easy to hear and then ignore that we use and discard 1 million plastic cups every 6 hours on airline flights alone. When we're confronted with his powerful image, we engage with that fact on an entirely different level (and may never see airline plastic cups in the same way again).

The ability to see data expands our perceptions of the world in ways that we're just beginning to understand. Computer simulations allow us to see how diseases spread, how forest fires might be contained, how terror networks communicate. We gain understanding of these things in ways that were unimaginable only a few decades ago. When Blaise Aguera y Arcas demonstrates Photosynth, we feel as if we're looking at the future. By linking together user-contributed digital images culled from all over the Internet, he creates navigable "immensely rich virtual models of every interesting part of the earth" created from the collective memory of all of us. Deb Roy does somewhat the same thing with language, pulling in publicly available social media feeds to analyze national and global conversation trends.

Roy sums it up with these powerful words: "What's emerging is an ability to see new social structures and dynamics that have previously not been seen. ...The implications here are profound, whether it's for science, for commerce, for government, or perhaps most of all, for us as individuals."

Let's begin with the TEDTalk from David McCandless, a self-described "data detective" who describes how to highlight hidden patterns in data through its artful representation.

David McCandless

The beauty of data visualization.

i. Data obtained June 2012 from “How Much Data Is Created Every Minute?” on http://mashable.com/2012/06/22/data-created-every-minute/ .

Relevant talks

Hans Rosling

The magic washing machine.

Nathalie Miebach

Art made of storms.

Chris Jordan

Turning powerful stats into art.

Blaise Agüera y Arcas

How photosynth can connect the world's images.

The birth of a word

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• Information Science and Technology
• Social Issues

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Essay Samples on Statistics

The importance of statistics in daily life: quantifying reality.

Statistics may seem like an abstract field reserved for researchers and analysts, but its influence extends far beyond academic settings. In fact, statistics play a vital role in shaping our daily lives, informing decision-making, guiding policies, and helping us make sense of complex information. From...

Models and Methods of Wind Speed Forecasting

Wind power energy has a significant importance in the electric grid. As it is an inexhaustible and freely available resource, its usage is globally increasing. It is replacing all other costly exhaustible resources that were used so far. So, accurate prediction of wind power is...

Two Types of Statistics: Descriptive and Inferential

Statistics is sub field of both science and mathematics as it uses the rules from these two fields. It works with the calculations and quantifications of data, scrutinizing it, elucidating, and dispensing it in the best form. The students of this subject collect the data...

Introduction To Statistics: Definition, Benefits, Methods

Definition Branch of mathematics concerned with collection, classification, analysis, and interpretation of numerical facts, for drawing inferences on the basis of their quantifiable likelihood (probability). Statistics can interpret aggregates of data too large to be intelligible by ordinary observation because such data (unlike individual quantities)...

• Qualitative Research

International Business' Statistics of Import and Manufacturing Process

Introduction Cambridge dictionary defines international business as the activity of trading goods and services between countries. However international business is beyond this definition, it has a very wide scope. Basically, international business is a cross border transaction between individuals, business or government entities. The transaction...

• International Business

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The Affects of Demographic Statistics on Human Growth and Other Factors

Demography is the statistical sample of people, particularly in terms of size and density, distribution and vital statistics such as births, marriages, deaths, etc. History has its own ups and downs forever, and demography has been helping us since forever to study the effect of...

• Population Growth

Best topics on Statistics

1. The Importance of Statistics in Daily Life: Quantifying Reality

2. Models and Methods of Wind Speed Forecasting

3. Two Types of Statistics: Descriptive and Inferential

4. Introduction To Statistics: Definition, Benefits, Methods

5. International Business’ Statistics of Import and Manufacturing Process

6. The Affects of Demographic Statistics on Human Growth and Other Factors

• Space Exploration
• Mechanical Engineering
• Hermit Crab
• Charles Darwin

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Statistical Research Questions: Five Examples for Quantitative Analysis

Table of contents, introduction.

How are statistical research questions for quantitative analysis written? This article provides five examples of statistical research questions that will allow statistical analysis to take place.

In quantitative research projects, writing statistical research questions requires a good understanding and the ability to discern the type of data that you will analyze. This knowledge is elemental in framing research questions that shall guide you in identifying the appropriate statistical test to use in your research.

Thus, before writing your statistical research questions and reading the examples in this article, read first the article that enumerates the  four types of measurement scales . Knowing the four types of measurement scales will enable you to appreciate the formulation or structuring of research questions.

Once you feel confident that you can correctly identify the nature of your data, the following examples of statistical research questions will strengthen your understanding. Asking these questions can help you unravel unexpected outcomes or discoveries particularly while doing exploratory data analysis .

Five Examples of Statistical Research Questions

In writing the statistical research questions, I provide a topic that shows the variables of the study, the study description, and a link to the original scientific article to give you a glimpse of the real-world examples.

Topic 1: Physical Fitness and Academic Achievement

A study was conducted to determine the relationship between physical fitness and academic achievement. The subjects of the study include school children in urban schools.

Statistical Research Question No. 1

Is there a significant relationship between physical fitness and academic achievement?

Notice that this study correlated two variables, namely 1) physical fitness, and 2) academic achievement.

To allow statistical analysis to take place, there is a need to define what is physical fitness, as well as academic achievement. The researchers measured physical fitness in terms of  the number of physical fitness tests  that the students passed during their physical education class. It’s simply counting the ‘number of PE tests passed.’

On the other hand, the researchers measured academic achievement in terms of a passing score in Mathematics and English. The variable is the  number of passing scores  in both Mathematics and English.

Both variables are ratio variables. 

Given the statistical research question, the appropriate statistical test can be applied to determine the relationship. A Pearson correlation coefficient test will test the significance and degree of the relationship. But the more sophisticated higher level statistical test can be applied if there is a need to correlate with other variables.

In the particular study mentioned, the researchers used  multivariate logistic regression analyses  to assess the probability of passing the tests, controlling for students’ weight status, ethnicity, gender, grade, and socioeconomic status. For the novice researcher, this requires further study of multivariate (or many variables) statistical tests. You may study it on your own.

Most of what I discuss in the statistics articles I wrote came from self-study. It’s easier to understand concepts now as there are a lot of resource materials available online. Videos and ebooks from places like Youtube, Veoh, The Internet Archives, among others, provide free educational materials. Online education will be the norm of the future. I describe this situation in my post about  Education 4.0 .

The following video sheds light on the frequently used statistical tests and their selection. It is an excellent resource for beginners. Just maintain an open mind to get rid of your dislike for numbers; that is, if you are one of those who have a hard time understanding mathematical concepts. My ebook on  statistical tests and their selection  provides many examples.

Source: Chomitz et al. (2009)

Topic 2: Climate Conditions and Consumption of Bottled Water

This study attempted to correlate climate conditions with the decision of people in Ecuador to consume bottled water, including the volume consumed. Specifically, the researchers investigated if the increase in average ambient temperature affects the consumption of bottled water.

Statistical Research Question No. 2

Is there a significant relationship between average temperature and amount of bottled water consumed?

In this instance, the variables measured include the  average temperature in the areas studied  and the  volume of water consumed . Temperature is an  interval variable,  while volume is a  ratio variable .

In this example, the variables include the  average temperature  and  volume of bottled water . The first variable (average temperature) is an interval variable, and the latter (volume of water) is a ratio variable.

Now, it’s easy to identify the statistical test to analyze the relationship between the two variables. You may refer to my previous post titled  Parametric Statistics: Four Widely Used Parametric Tests and When to Use Them . Using the figure supplied in that article, the appropriate test to use is, again, Pearson’s Correlation Coefficient.

Source: Zapata (2021)

Topic 3: Nursing Home Staff Size and Number of COVID-19 Cases

An investigation sought to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 3

Is there a significant relationship between the number of unique employees working in skilled nursing homes and the following:

• number of weekly confirmed COVID-19 cases among residents and staff, and
• number of weekly COVID-19 deaths among residents.

Note that this study on COVID-19 looked into three variables, namely 1) number of unique employees working in skilled nursing homes, 2) number of weekly confirmed cases among residents and staff, and 3) number of weekly COVID-19 deaths among residents.

We call the variable  number of unique employees  the  independent variable , and the other two variables ( number of weekly confirmed cases among residents and staff  and  number of weekly COVID-19 deaths among residents ) as the  dependent variables .

This correlation study determined if the number of staff members in nursing homes influences the number of COVID-19 cases and deaths. It aims to understand if staffing has got to do with the transmission of the deadly coronavirus. Thus, the study’s outcome could inform policy on staffing in nursing homes during the pandemic.

A simple Pearson test may be used to correlate one variable with another variable. But the study used multiple variables. Hence, they produced  regression models  that show how multiple variables affect the outcome. Some of the variables in the study may be redundant, meaning, those variables may represent the same attribute of a population.  Stepwise multiple regression models  take care of those redundancies. Using this statistical test requires further study and experience.

Source: McGarry et al. (2021)

Topic 4: Surrounding Greenness, Stress, and Memory

Scientific evidence has shown that surrounding greenness has multiple health-related benefits. Health benefits include better cognitive functioning or better intellectual activity such as thinking, reasoning, or remembering things. These findings, however, are not well understood. A study, therefore, analyzed the relationship between surrounding greenness and memory performance, with stress as a mediating variable.

Statistical Research Question No. 4

Is there a significant relationship between exposure to and use of natural environments, stress, and memory performance?

As this article is behind a paywall and we cannot see the full article, we can content ourselves with the knowledge that three major variables were explored in this study. These are 1) exposure to and use of natural environments, 2) stress, and 3) memory performance.

Referring to the abstract of this study,  exposure to and use of natural environments  as a variable of the study may be measured in terms of the days spent by the respondent in green surroundings. That will be a ratio variable as we can count it and has an absolute zero point. Stress levels can be measured using standardized instruments like the  Perceived Stress Scale . The third variable, i.e., memory performance in terms of short-term, working memory, and overall memory may be measured using a variety of  memory assessment tools as described by Murray (2016) .

As you become more familiar and well-versed in identifying the variables you would like to investigate in your study, reading studies like this requires reading the method or methodology section. This section will tell you how the researchers measured the variables of their study. Knowing how those variables are quantified can help you design your research and formulate the appropriate statistical research questions.

Source: Lega et al. (2021)

Topic 5: Income and Happiness

This recent finding is an interesting read and is available online. Just click on the link I provide as the source below. The study sought to determine if income plays a role in people’s happiness across three age groups: young (18-30 years), middle (31-64 years), and old (65 or older). The literature review suggests that income has a positive effect on an individual’s sense of happiness. That’s because more money increases opportunities to fulfill dreams and buy more goods and services.

Reading the abstract, we can readily identify one of the variables used in the study, i.e., money. It’s easy to count that. But for happiness, that is a largely subjective matter. Happiness varies between individuals. So how did the researcher measured happiness? As previously mentioned, we need to see the methodology portion to find out why.

If you click on the link to the full text of the paper on pages 10 and 11, you will read that the researcher measured happiness using a 10-point scale. The scale was categorized into three namely, 1) unhappy, 2) happy, and 3) very happy.

An investigation was conducted to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 5

Is there a significant relationship between income and happiness?

Source: Måseide (2021)

Now the statistical test used by the researcher is, honestly, beyond me. I may be able to understand it how to use it but doing so requires further study. Although I have initially did some readings on logit models, ordered logit model and generalized ordered logit model are way beyond my self-study in statistics.

Anyhow, those variables found with asterisk (***, **, and **) on page 24 tell us that there are significant relationships between income and happiness. You just have to look at the probability values and refer to the bottom of the table for the level of significance of those relationships.

I do hope that upon reaching this part of the article, you are now well familiar on how to write statistical research questions. Practice makes perfect.

References:

Chomitz, V. R., Slining, M. M., McGowan, R. J., Mitchell, S. E., Dawson, G. F., & Hacker, K. A. (2009). Is there a relationship between physical fitness and academic achievement? Positive results from public school children in the northeastern United States.  Journal of School Health ,  79 (1), 30-37.

Lega, C., Gidlow, C., Jones, M., Ellis, N., & Hurst, G. (2021). The relationship between surrounding greenness, stress and memory.  Urban Forestry & Urban Greening ,  59 , 126974.

Måseide, H. (2021). Income and Happiness: Does the relationship vary with age?

McGarry, B. E., Gandhi, A. D., Grabowski, D. C., & Barnett, M. L. (2021). Larger Nursing Home Staff Size Linked To Higher Number Of COVID-19 Cases In 2020: Study examines the relationship between staff size and COVID-19 cases in nursing homes and skilled nursing facilities. Health Affairs, 40(8), 1261-1269.

Zapata, O. (2021). The relationship between climate conditions and consumption of bottled water: A potential link between climate change and plastic pollution. Ecological Economics, 187, 107090.

© P. A. Regoniel 12 October 2021 | Updated 08 January 2024

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How to write survey questions, about the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

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Statistics Research Topics: Ideas & Questions

June 16, 2023

Looking for research topics in statistics? Whether you’re a student working on a class project or a researcher in need of inspiration, finding the right topic can be challenging. With numerous areas to explore in statistics, narrowing down your options can be overwhelming. But with some creativity and research, you can find an interesting and relevant topic. This article offers ideas and examples of statistics research topics to consider, so let’s dive in!

Statistics Research: What It Comprises

The data collected by statistics research can be quantitative (numbers) or qualitative (text). The data can also be presented in tables or graphs for easy understanding by the audience. However, it is not always necessary to present the data in the form of tables or graphs, as sometimes the raw data can be good enough to convey the message from the researcher.

In statistics projects, the researchers usually design experiments to test specific hypotheses about a population’s characteristics or behavior. For example, suppose you want to know whether people who wear glasses will have better eyesight than those who don’t wear glasses. In that case, you need to collect information about their vision before and after wearing glasses (experimental group) and compare their vision with those who do not wear glasses (control group). You would then find out whether there was any difference between these two groups with respect to eyesight improvement due to wearing glasses.

Tips on How to Choose a Statistics Research Topic

Firstly, remember that a good statistics topic should interest you and also have a substantial amount of data available for analysis. Once you have decided on your topic, you can collect data for your study using secondary sources or conducting primary research through surveys or interviews.

You can also use search engines like Google or Yahoo! to find information about your topic of interest. You can use keywords like “income disparity” or “inequality causes” to find relevant websites on which you can find information related to your topic of interest.

Next, consider what types of questions your supervisor would like answered with this data type. For example, if you’re looking at crime rates in your city, maybe they would like to know which areas have higher crime rates than others to plan police patrols accordingly. Or maybe they just want to know whether there’s any correlation between high crime rates and low-income neighborhoods (there probably will be).

Feel free to select any topic and try our free AI essay generator to craft your essay.

Statistics Research Topics in Business

• Understanding the factors that influence consumer purchase decisions in the technology industry
• Advertising and sales revenue: a time-series analysis
• The effectiveness of customer loyalty programs in increasing customer retention and revenue
• The relationship between employee job satisfaction and productivity
• The factors that contribute to employee turnover in the hospitality industry
• Product quality on customer satisfaction and loyalty: a longitudinal study
• The application of social media marketing in increasing brand awareness and customer engagement
• Corporate social responsibility (CSR) initiatives and brand reputation: a meta-analysis
• Understanding the factors that influence customer satisfaction in the restaurant industry
• E-commerce on traditional brick-and-mortar retail sales: a comparative analysis
• The effectiveness of supply chain management strategies in reducing operational costs and improving efficiency
• The relationship between market competition and innovation: a cross-country analysis
• Understanding the factors that influence employee motivation and engagement in the workplace
• Business analytics on strategic decision-making: a case study approach
• The effectiveness of performance-based incentives in increasing employee productivity and job satisfaction
• Organizational performance dependence on employee diversity and organizational performance
• Understanding the factors that contribute to startup success in the technology industry
• The impact of pricing strategies on sales revenue and profitability
• The effectiveness of corporate training programs in improving employee skill development and performance
• The relationship between brand image and customer loyalty

Research Topics in Applied Statistics

• The impact of educational attainment on income level
• The effectiveness of different advertising strategies in increasing sales
• The relationship between socioeconomic status and health outcomes
• The effectiveness of different teaching methods in promoting academic success
• The impact of job training programs on employment rates
• The relationship between crime rates and community demographics
• Different medication dosages in treating a particular condition
• The influence of environmental pollutants on health outcomes
• The interconnection between access to healthcare and health outcomes
• The effectiveness of different weight loss programs in promoting weight loss
• The impact of social support on mental health outcomes
• The relationship between demographic factors and political affiliation
• The effectiveness of different exercise programs in promoting physical fitness
• The influence of parenting styles on child behavior
• The relationship between diet and chronic disease risk
• Different smoking cessation programs for promoting smoking cessation
• The impact of public transportation on urban development
• The relationship between technology usage and social isolation
• The effectiveness of different stress reduction techniques in reducing stress levels
• The influence of climate change on crop

Statistics Research Topics in Psychology

• The correlation between childhood trauma and adult depression
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders
• The impact of social media on self-esteem and body image in adolescents
• Personality traits and job satisfaction: how are they related?
• The prevalence and predictors of bullying in schools
• The effects of sleep deprivation on cognitive performance
• The role of parenting styles in the development of emotional intelligence
• The effectiveness of mindfulness-based interventions in reducing stress and anxiety
• The impact of childhood abuse on adult relationship satisfaction
• The influence of social support on coping with chronic illness
• The factors that contribute to successful aging
• The prevalence and predictors of addiction relapse
• The impact of cultural factors on mental health diagnosis and treatment
• Exercise and mental health: in which way are they connected?
• The effectiveness of art therapy in treating trauma-related disorders
• The prevalence and predictors of eating disorders in college students
• The influence of attachment styles on romantic relationships
• The effectiveness of group therapy in treating substance abuse disorders
• The prevalence and predictors of postpartum depression
• The impact of childhood socioeconomic

Sports Statistics Research Topics

• The relationship between player performance and team success in the National Football League (NFL)
• Understanding the factors that influence home-field advantage in professional soccer
• The impact of game-day weather conditions on player performance in Major League Baseball (MLB)
• The effectiveness of different training regimens in improving endurance and performance in long-distance running
• The relationship between athlete injury history and future injury risk in professional basketball
• The impact of crowd noise on team performance in college football
• The effectiveness of sports psychology interventions in improving athlete performance and mental health
• The relationship between player height and success in professional basketball: a regression analysis
• Understanding the factors that contribute to the development of youth soccer players in the United States
• The influence of playing surface on injury rates in professional football: a longitudinal study
• The effectiveness of pre-game routines in improving athlete performance in tennis
• The relationship between athletic ability and academic success among college athletes
• Understanding the factors that influence injury risk and recovery time in professional hockey players
• The impact of in-game statistics on coaching decisions in professional basketball
• The effectiveness of different dietary regimens in improving athlete performance in endurance sports
• The relationship between athlete sleep habits and performance: a longitudinal study
• Understanding the factors that influence athlete endorsement deals and sponsorships in professional sports
• The influence of stadium design on crowd noise levels and player performance in college football
• The effectiveness of different strength training regimens in improving athlete performance in track and field events
• The relationship between player salary and team success in professional baseball: a longitudinal analysis

Survey Methods Statistics Research Topics

• Understanding the factors that influence response rates in online surveys
• The effectiveness of different survey question formats in eliciting accurate and reliable responses
• The relationship between survey mode (phone, online, mail) and response quality in political polling
• The impact of incentives on survey response rates and data quality
• Understanding the factors that contribute to respondent satisfaction in surveys
• The effectiveness of different sampling methods in achieving representative samples in survey research
• The relationship between survey item order and response bias: a meta-analysis
• The impact of social desirability bias on survey responses: a longitudinal study
• Understanding the factors that influence survey question wording and response bias
• The effectiveness of different visual aids in improving respondent comprehension and response quality
• The relationship between survey timing and response rate: a comparative analysis
• The impact of interviewer characteristics on survey response quality in face-to-face surveys
• Understanding the factors that contribute to nonresponse bias in survey research
• The effectiveness of different response scales in measuring attitudes and perceptions in surveys
• The relationship between survey length and respondent engagement: a cross-sectional analysis
• The influence of skip patterns on survey response quality and completion rates
• Understanding the factors that influence survey item nonresponse and item refusal rates
• The effectiveness of pre-testing and piloting surveys in improving data quality and reliability
• The relationship between survey administration and response quality: a comparative analysis of phone, online, and in-person surveys
• The impact of survey fatigue on response quality and data completeness: a longitudinal study

As mentioned above, statistics is the science of collecting and analyzing data to draw conclusions and make predictions. To conduct a proper statistical analysis, you must first define your research question, gather data from various sources, analyze the information, and draw conclusions based on the results.

This process can be challenging for many people who do not have an extensive background in statistics. However, it does not have to be so tricky if you use our professional Custom Writing help. Our writers are highly qualified professionals who will work with you to develop a clear understanding of your research problem and then guide you through every step of the process. We will also ensure that your paper follows all academic standards to meet all requirements for originality and quality.

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Statistics Issues Suitable for Academic Debate

If you’re struggling with your essay writing task in the field of statistics, you have to start with a winning topic. Using our specific statistics essay topics for inspiration, you may come up with a brand-new issue to discuss in your essay or choose one from the list and work on it from A to Z:

• The Results of the Collaborative Work Between Egon Pearson and Jerzy Neyman
• Importance of Statistics and Mathematics to Economics
• Dynamic Bradley-Terry Modeling of Sports Tournaments
• A Statistical Analysis of Crime Offenses Recorded in Nevada
• The Law of Large Numbers (LLN) to Guarantee Stable Long-Term Results for the Averages of Some Random Events
• Importance of Statistics in the Area of Educational Management
• Statistics, Estimators and Pivotal Quantities
• Stochastic Music by Iannis Xenakis: Predicative Ways to Create Art
• Advantages and Disadvantages of Official Statistics in the Research in the Sociology Field
• Bayesian Probability as an Interpretation of the Concept of Probability
• Analysis of Loss Systems with Overlapping Resource Requirements
• Measurement Processes That Generate Statistical Data
• Early Applications of Statistical Thinking in the 17th Century
• Statistical Modeling of Swimming Microorganisms
• Draft Statistics on Health Care Prescription Errors in the USA
• Definition and Meaning of a Statistical Error
• Sir Arthur Lyon Bowley, the Pioneer of the Use of Sampling Techniques in Social Surveys
• Three Kinds of Lies: Lies, Damned Lies, and Statistics
• Methods of Statistics Combined with Chaos Theory and Fractal Geometry
• The Relationship Between Dependent (Output) Variables and Independent (Input) Variables

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Importance of Statistics in Daily Life Essay

In order to explore the use of statistics in everyday life, the essay should start with a debatable statement. On a daily basis, people collect and analyze a lot of information presented in numbers, and it is closely associated with different aspects of their lives. Thus, it is typical to apply the elementary statistical approaches to examining the learned material about everyday activities. It can help to get the average results in relation to actual events or phenomena.

However, many people do not guess that they use such principles as the base for their knowledge. So, to reveal the importance of statistics in daily life, this essay will provide different real-life examples and explain the application of various data analysis methods.

Moreover, when persons have to present the solution to this or that question or decide how to act in the definite situation they also use the statistical data on the issue as one of the main arguments which can influence the further development of the case. That is why statistics can be defined as the science which deals with the data’s collection and its interpretation according to the certain task, and the results of the research can be effectively used in many spheres. From this point, the relative value of statistics for the everyday life is in the fact that people have the opportunity to plan their actions according to the statistical data with references to those results which can satisfy or not their expectations.

People are usually interested in the average temperature and the weather forecasts, in the amount of people who prefer this or that product which they usually purchase. These persons listen to the economical news in which the data of statistics on the state’s development are presented and pay attention to the risks of the transport incidents before going out the house. The statistical data influence all the aspects of the people’s life during the whole day.

When an individual wants to learn about the latest news he concentrates on the information which is interesting for him personally, and these facts are often given in the form of numbers. The average results in different fields and areas from the average level of incomes in the country and the average level of attendance the local library till the average data on the consumers’ preference of brands and services can provide the basics for the people’s choices and usual decisions which are made as a part of the daily rituals and routines.

One more effective advantage of statistics is the possibility to offer the prognoses of the development of definite situations and processes. People are inclined to use the statistical prognoses when they plan such significant changes in their life as the search of the new job, new investments in companies, travelling, and long-term projects. Statistics as the science is based on the strict mathematical calculations and formulas (Bluman, 2009). That is why its methods can be discussed as the effective ways of interpreting the collected quantitative information on any aspect of the life.

It is possible to analyze the tendencies of the world’s development with references to the statistical approach and use this approach as the means to organize the everyday life according to these trends. Furthermore, many people focus on the results of the statistical researches not only at the elementary level in their daily life but also as the part of their work. Thus, accounting, economics, logistics, and many other spheres of the knowledge use statistics (Black, 2009). Moreover, working with their computers, people often refer to the statistical analysis of the data in order to receive the average result or form the picture of the process’s development (Mann, 2010).

Today, it is not necessary for people to examine and test a lot of material to get the information about its appropriateness for the people’s everyday activity or about tendencies of the phenomenon’s progress because all these data can be taken in the form of the statistical graphs or percentages. There are many daily questions the answers to which are hidden in the statistical data.

Black, K. (2009). Business statistics: Contemporary decision making . USA: Wiley.

Bluman, A. G. (2009). Elementary statistics: A step by step approach. New York, NY: McGraw-Hill.

Mann, P. S. (2010). Introductory statistics . Hoboken, NJ: Wiley.

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IvyPanda. (2023, October 30). Importance of Statistics in Daily Life Essay. https://ivypanda.com/essays/statistics-in-the-everyday-life/

"Importance of Statistics in Daily Life Essay." IvyPanda , 30 Oct. 2023, ivypanda.com/essays/statistics-in-the-everyday-life/.

IvyPanda . (2023) 'Importance of Statistics in Daily Life Essay'. 30 October.

IvyPanda . 2023. "Importance of Statistics in Daily Life Essay." October 30, 2023. https://ivypanda.com/essays/statistics-in-the-everyday-life/.

1. IvyPanda . "Importance of Statistics in Daily Life Essay." October 30, 2023. https://ivypanda.com/essays/statistics-in-the-everyday-life/.

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IvyPanda . "Importance of Statistics in Daily Life Essay." October 30, 2023. https://ivypanda.com/essays/statistics-in-the-everyday-life/.

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3 Essay Questions- Statistics, statistics homework help

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Essay 1 A group of researchers conducted an experiment to determine which vaccine is more effective for preventing getting the flu. They tested two different types of vaccines: a shot and a nasal spray. To test the effectiveness, 1000 participants were randomly selected with 500 people getting the shot and 500 the nasal spray. Of the 500 people were treated with the shot, 80 developed the flu and 420 did not. Of the people who were treated with the nasal spray, 120 people developed the flu and 380 did not. The level of significance was set at .05. The proportion of people who were treated with the shot who developed the flu = .16, and the proportion of the people who were treated with the nasal spray was .24. The calculated p value = .0008. For this essay, describe the statistical approaches (e.g., identify the hypotheses and research methods) used in this excerpt from a research study. Interpret the statistical results and examine the limitations of the statistical methods. Finally, evaluate the research study as a whole and apply what you have learned about hypothesis testing and inferential statistics by discussing how you might conduct a follow-up study.

Your essay must address the following points:

• What were the null and alternative hypotheses?
• Were the results of this test statistically significant?
• If so, why were they significant?
• Would the researchers reject or fail to reject the null hypothesis?
• Do the results provide sufficient evidence to support the alternative hypothesis?
• Was the sample appropriate for this study? Explain your answer.
• What are some possible limitations to this study?
• Discuss how you would conduct a follow up study to this one. Explain your answer.

Essay 2 A researcher has investigated the relationship between IQ and grade point average (GPA) and found the correlation to be .75.

• Evaluate the correlational result and identify the strength of the correlation.
• Examine the assumptions and limitations of the possible connection between the researcher’s chosen variables.
• Identify and describe other statistical tests that could be used to study this relationship.
• Is this a positive or negative correlation?
• What does this correlation mean?
• Does this correlation imply that individuals with high Intelligence Quotients (IQ) have high Grade Point Averages (GPA)?
• What other variables might be influencing this relationship?
• What is the connection between correlation and causation?
• What are some of the factors that affect the size of this correlation?
• If not, what statistical tests should a researcher use, and why?Describe the difference between practical and statistical significance.

Essay 3 A researcher has recorded the reaction times of 20 individuals on a memory assessment. The following table indicates the individual times:

In this essay, demonstrate your ability to organize data into meaningful sets, calculate basic descriptive statistics, interpret the results, and evaluate the effects of outliers and changes in the variables. You may use Excel, one of the many free online descriptive statistics calculators, or calculate the values by hand and/or with a calculator.

Next, separate the data into two groups of 10; one group will be the lower reaction times, and the second group will be the higher reaction times. Then, address the following points in your essay response:

• Calculate the sum, mean, mode, median, standard deviation, range, skew, and kurtosis for each group.
• How do the two groups differ?
• Are there any outliers in either data group?
• What effect does an outlier have on a sample?

Lastly, double each sample by repeating the same 10 data points in each group. You will have a total of 20 data points for each group. After completing this, address the following in your essay response:

• Calculate the following for the new data groups: sum, mean, mode, median, standard deviation, range, skew, and kurtosis.
• Did any of the values change?
• How does sample size affect those values?

Explanation & Answer

Thank you very much for the opportunityyou're really only needing the .docx files, Excel's was for calculating formulas and outlines basically describe what we did per essay Essay 3 A researcher has recorded the reaction times of 20 individuals on a memory assessment. The following table indicates the individual times: 4.7 7.3 4.1 2.2 9.5 15.2 4.3 9.5 2.7 3.1 9.2 2.9 8.2 7.6 3.5 2.5 9.3 4.8 8.5 8.1 In this essay, demonstrate your ability to organize data into meaningful sets, calculate basic descriptive statistics, interpret the results, and evaluate the effects of outliers and changes in the variables. You may use Excel, one of the many free online descriptive statistics calculators, or calculate the values by hand and/or with a calculator. Next, separate the data into two groups of 10; one group will be the lower reaction times, and the second group will be the higher reaction times. Then, address the following points in your essay response: o o o o Calculate the sum, mean, mode, median, standard deviation, range, skew, and kurtosis for each group. How do the two groups differ? Are there any outliers in either data group? What effect does an outlier have on a sample? Lastly, double each sample by repeating the same 10 data points in each group. You will have a total of 20 data points for each group. After completing this, address the following in your essay response: o o o Calculate the following for the new data groups: sum, mean, mode, median, standard deviation, range, skew, and kurtosis. Did any of the values change? How does sample size affect those values? Last Name 1 You’re Name Professors Name Course Title Submission Date Essay 3: Memory Assessment 1. Calculate the sum, mean, mode, median, standard deviation, range, skew, and kurtosis for each group. 2.2 4.7 7.3 4.1 9.5 2.7 8....

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