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Statistics to Support Research: Why & How to Use Statistics

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Why Use Statistics?

The Research Process

• The Research Process by Emily Henderson Last Updated Aug 13, 2024 9326 views this year

Using Statistics in Your Writing

• Writing with Statistics  The Purdue Online Writing Lab explains how to write with statistics including quick tips, writing descriptive statistics, writing inferential statistics, and using visuals with statistics. 
• This Statistics handout from The Writing Center, University of North Carolina at Chapel Hill, helps you to use statistics to make your argument as effectively as possible.
• For a better understanding of why and how to use statistics in your writing, read the chapter on "Arguing" in The Norton Field Guide to Writing , pages 397-417. Copies are on reserve at the Circulation Desk in Columbus Hall and are also available at the Reference Desk in Moeller Hall at the Delaware campus.  
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Writing with Descriptive Statistics

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Usually there is no good way to write a statistic. It rarely sounds good, and often interrupts the structure or flow of your writing. Oftentimes the best way to write descriptive statistics is to be direct. If you are citing several statistics about the same topic, it may be best to include them all in the same paragraph or section.

The mean of exam two is 77.7. The median is 75, and the mode is 79. Exam two had a standard deviation of 11.6.

Overall the company had another excellent year. We shipped 14.3 tons of fertilizer for the year, and averaged 1.7 tons of fertilizer during the summer months. This is an increase over last year, where we shipped only 13.1 tons of fertilizer, and averaged only 1.4 tons during the summer months. (Standard deviations were as followed: this summer .3 tons, last summer .4 tons).

Some fields prefer to put means and standard deviations in parentheses like this:

If you have lots of statistics to report, you should strongly consider presenting them in tables or some other visual form. You would then highlight statistics of interest in your text, but would not report all of the statistics. See the section on statistics and visuals for more details.

If you have a data set that you are using (such as all the scores from an exam) it would be unusual to include all of the scores in a paper or article. One of the reasons to use statistics is to condense large amounts of information into more manageable chunks; presenting your entire data set defeats this purpose.

At the bare minimum, if you are presenting statistics on a data set, it should include the mean and probably the standard deviation. This is the minimum information needed to get an idea of what the distribution of your data set might look like. How much additional information you include is entirely up to you. In general, don't include information if it is irrelevant to your argument or purpose. If you include statistics that many of your readers would not understand, consider adding the statistics in a footnote or appendix that explains it in more detail.

Writing With Statistics: Mistakes to Watch Out For

Numbers are power. Adding relevant statistics to your content can strengthen any argument. But if not used carefully, numbers create more problems than they solve.

I wrote a book for content writers called From Reads To Leads . You should check it out to learn what rules you need to follow to write content that converts readers into leads. One of these rules is about using statistics in writing. Go to my home page to get the book or read the first chapter.

Many writers pick up numbers off the street to make their messages more compelling. They aren’t looking to support their arguments or to make their stories more accurate. They aren’t looking for truth. 

Let's look at this example:

If 36 percent of Americans use food delivery services, does this mean that the popularity of these services is growing? To show growth in popularity, we would need to compare the percentage of Americans who used food delivery services in March 2019, for example, with the percentage who used them in March 2021. Growth can only be demonstrated over time. Since there’s nothing to which readers can compare this 36 percent, they might doubt whether the popularity of food delivery services is actually growing.

Let's look at another example:

Any percentage is meaningless to your readers unless you compare it against some base percentage. The stats in the example I've just mentioned look reasonable. The author compares Black Friday sales completed using mobile devices last year with sales completed using mobile devices a few years ago. But let’s think about it for a second. Don’t these statistics raise any questions? Firstly of all, they come from two different sources. They may have been collected using wildly different methodologies and by surveying entirely different demographics. There’s no way for the reader to know whether these percentages can reasonably be compared.

Sometimes a percentage might look high, but without context, it might not be telling the whole story. You need to dig deeper to uncover the truth:

The author did solid research to help her readers arrive at the conclusion that despite a seemingly large number of women-owned businesses, there’s still gender inequality in entrepreneurship.

How to write with statistics

As you use statistics in your writing, here are a few things you need to pay attention to:

1 . Numbers can be just as ambiguous as words and need just as much explanation.

For example:

Is 57 percent good or bad? This statement requires an explanation:

Now it’s clear that we’re making progress. 

2. Don’t just throw numbers everywhere you can because it’s considered a good SEO practice. Your statistics need to help you make your point . They can make your arguments believable. 

For example, let’s say our key message is “eating fat keeps you healthy.” One of the arguments we can use to support this message is that polyunsaturated fats can lower cholesterol levels, which, as a result, can lower the risk of heart disease and stroke. We can use statistics to make this argument believable:

‍ We didn’t use these stats to demonstrate expertise . We added them to support our key message.

3. When trying to prove your argument with statistics, you need to be sure that what you’re saying is true. I you have doubts, you need to look first at the numbers to help you shape your message and ideas. Don’t do it the other way round. 

If you’ve already shaped your message about something, you’ll be tempted to look for data to prove that you’re right. But the truth is, you might be wrong. You can find seemingly legitimate evidence to support any claim, but your argument won’t be convincing if it’s built on a shaky foundation. 

The next time you find yourself thinking that what you want to say might not be accurate, don’t head to Google to prove you’re right. Instead, look to answer the question with data. Good writing is about telling the truth, not trying to dupe the reader.

4. Numbers without context or specific details are just that—numbers. They don’t help readers arrive at conclusions.

Now let’s see how to author, uses details to communicate the gravity of the situation:

The statement “the cost of college increased by more than 25% in the last 10 years” doesn’t give readers a clear idea of the growing cost of higher education. But comparing what students paid (on average) for a college education in 1978, 2008, and today helps the reader realize that the costs of college are increasing at a breakneck pace and that something has to be done about it.

You need statistics to prove your arguments. And when citing statistics, you must obey the same rules of clarity you obey with words. Learn more about these rules in my book From Reads To Leads.

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Why Are Statistics in Psychology Necessary?

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Some psychology students are surprised (maybe even dismayed) to realize that they must take a statistics course. But almost all psychology programs have this requirement for graduation. The subject is also discussed in many other classes, highlighting the importance of statistics in psychology.

Recognizing the role of statistics in psychology is necessary when pursuing careers in this field , and may even make it easier to accept having to take this type of course. It can also be beneficial to know what type of statistics classes may be required when majoring in psychology, along with how to get help if this subject feels challenging to learn.

At a Glance

Statistics enable us to organize, describe, and understand large amounts of data. Understanding statistics in psychology can help you excel both in your classes and in this field. Many college programs have different statistics class requirements. If you struggle with learning statistics, talking with the course's instructor or joining a study group can help.

The Importance of Statistics in Psychology

Consider the sheer volume of data we encounter in a given day. How many hours did you sleep? How many students in your class ate breakfast this morning? How many people live within a one-mile radius of your home? By using statistics, we can organize and interpret all this information in a meaningful way.

In psychology, we are also confronted with enormous amounts of data. Statistics allow psychologists to:

• Organize data : When dealing with huge amounts of information, it's all too easy to become overwhelmed. Statistics enable psychologists to organize data in ways that are easier to comprehend. Visual displays such as graphs, pie charts, frequency distributions , and scatterplots provide researchers with a better overview of the information, making it easier to find patterns they might otherwise miss.
• Describe data : Think about what happens when researchers collect a great deal of information about a group of people. An example of this would be the U.S. Census. Descriptive statistics provide a way to summarize data such as the number of adults versus children or the percentage of the population that is currently employed.
• Make inferences based on data : By using what's known as inferential statistics, researchers can draw conclusions about a given sample or population. Psychologists use statistics to help determine whether their hypothesis should be accepted or rejected.

Benefits of Statistics in Psychology

In psychology research, there are often more questions than answers. How do changes in one variable impact other variables? Is there a way to demonstrate a relationship between variables ? What is the overall strength of this relationship and what does this mean? Statistics allow us to answer these kinds of questions.

Having an understanding of statistical methods can also help us excel in other classes. Whether taking a class in social psychology or human sexuality, a great deal of time is often spent learning about research. Developing a strong foundation of statistical knowledge allows us to make better sense of the research described in these psychology courses .

Plus, think about all the claims about psychology that we encounter outside of class on a daily basis. Magazines publish stories about the latest scientific findings, self-help books make proclamations about different ways to approach problems, and news reports interpret (or misinterpret) psychology research .

By understanding the research process—including the types of statistical analyses used—we become a wise consumer of psychology information and can make better judgments of the information we come across. Understanding statistics in psychology even enables us to make better decisions about our own health and well-being.

Statistics Requirements for Psychology Majors

Academic institutions have specific graduation requirements. Completing a certain number of math classes helps fulfill the school's general education requirements. For students majoring in psychology, statistics is a mathematics-based class that is also typically required.

Because every school is different, it's important to check the requirements for your specific institution. Look at both the school's general education requirements and the class requirements related to your desired psychology degree . This will tell you whether a statistics class is needed and, if so, which one or ones.

Getting Help With Statistics in Psychology

Understanding the importance of statistics in psychology can help students create a more positive mindset before even stepping into a statistics course. That said, we do recognize that this subject isn't always easy to learn.

Here's the good news: it's still possible to succeed in a stats class if you don't consider yourself "good at math." Some extra effort might be needed, but help is available.

Start with the class instructor. Ask about books, online tools, and on-campus resources that can help make your studies easier . Many colleges and universities offer a math lab where students can go to receive extra help and tutoring for any type of math course, statistics included.

Joining a study group is another option. If a statistics study group isn't available, you may even decide to create one. This can help not only you but others who may be struggling with the subject as well.

Tessler J. On the importance of learning statistics for psychology students . Association for Psychological Science.

Olsson-Collentine A, van Assen MALM, Hartgerink CHJ. The prevalence of marginally significant results in psychology over time .  Psychol Sci . 2019;30(4):576-586. doi:10.1177/0956797619830326

Gaertner S. How is the public being misled about research? Wiley.

Agnoli F, Wicherts JM, Veldkamp CL, Albiero P, Cubelli R. Questionable research practices among Italian research psychologists .  PLoS One . 2017;12(3):e0172792. doi:10.1371/journal.pone.0172792

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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How to use statistics in persuasive writing

Turn numbers into people and otherwise reframe the data.

Which is more dangerous? A disease that kills 1,286 out of every 10,000 people it strikes? Or one that kills 12.86% of its victims?

The former is about 20% more dangerous, said a group of college students, according to an article in Money magazine. In fact, 1,286 out of 10,000 is just a different expression of 12.86%.

“If you tell someone that something will happen to one out of 10 people,” Paul Slivic, University of Oregon psychologist, told Money, “they think, ‘Well, who’s the one?’”

To make statistics more compelling, make them more emotional.

Here are three more things to consider when choosing a frame for your data:

1. Choose a positive frame.

Which steak tastes better? A steak that’s 25% fat? Or one that’s 75% lean?

People in one study said they were more likely to buy the 75% lean steak rather than the 25% fat steak, even though they are the exact same steak (Johnson and Levin, 1985; Levin et al., 1985).

Call it the framing effect bias: People react differently to an option or idea based on how it is presented. So frame your products and positively instead of negatively.

When I wrote a charitable giving annual report, my clients were devastated to learn that 25% of Kansas City households and 21% of local businesses contributed nothing to charity. I wrote:

Today, 75% of Kansas City households and 79% of Kansas City businesses contribute to not-for-profit organizations.

Because it’s true. And it’s a better frame.

2. Tap the power of percentages.

How long would you be willing to walk to avoid a 25% Lyft or Uber fare increase? What about a 1.25x increase?

• Some 38% of people were willing to walk to avoid the 1.25x fare , according to a survey by Irrational Labs and Common Cents Lab .
• But 44% were willing to walk to avoid the 25% increase — even though 25% is the same amount as 1.25x.
• But wait! There’s more! People were more willing to walk 5 minutes to avoid a 25% increase vs. a 1.75x fare — even though 1.75 is 50 percentage points more than 25%.

Takeaway: Use absolute numbers for increases. But when offering discounts, stick with percentages.

3. Use like forms.

Which is bigger: three-quarters, 80% or seven out of 10? How much bigger?

Don’t make readers perform mathematical backflips to follow the numbers in your copy. Don’t compare apples to watermelons.

When you compare numbers, put them all in the same form. In this case: 75%, 80% and 70%.

Sources: I.P. Levin and G.J. Gaeth, “ How consumers are affected by the framing of attribute information before and after consuming the product ,” Journal of Consumer Research , No. 15, 1988, pp. 374-378

Gerd Gigerenzer and Adrian Edwards, “ Simple Tools for Understanding Risks: From Innumeracy to Insight ,” British Medical Journal , vol. 327, no. 7417, Sept. 27, 2003, pp. 741-744

TJ Larkin & Sandar Larkin, “Communicating Risk in Health Care,” Larkin Page , #27, February 2006

Amos Tversky and Daniel Kahneman, “ The Framing of Decisions and the Psychology of Choice ,” Science , Vol. 211, 30, January 1981

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Finding Statistics

• Why are Statistics Important?

Statistics are important because they help people make informed decisions. Governments, organizations, and businesses all collect statistics to help them track progress, measure performance, analyze problems, and prioritize. For example, the U.S. Census Bureau collects information from people about where they live and their age. This information can help cities decide where they should build a new hospital if they find that there is a high elderly population in an area or a new school, if they find there are many families with young children.

On a personal level, statistics can be a great way to enhance your argument in a research paper or presentation. They show that there is evidence to back up your claim and can add credibility to your work. Statistics often create an emotional response in your audience. Think about how you feel when someone can back up their argument with statistics? Don't the statistics make you feel more strongly to the argument?

The below video by Ms. Emma Stevenson will help explain how statistics can help you in a research paper or project:

Misleading Statistics

Statistics are an excellent way to enhance an argument and persuade your audience; however, there are some considerations to keep in mind. Statistics can be misleading, because they are often taken out of context. Sometimes, important information is left out about how the statistic was collected in order to make it seem more dramatic, proving big ideas or generalizations that it wouldn't if the rest of the information was included. 

For example, let's say you found a statistic that said 5 out of 5 dentists recommend a certain brand of toothpaste. That sounds like this is a great brand of toothpaste that everyone should use. However, what if you found out that the dentists were all asked if they would recommend that brand of toothpaste or not brushing your teeth at all? Of course all of the dentists are going to pick the brand of toothpaste. This makes the 5 out of 5 recommendation basically meaningless. You might assume when you see this statistic that dentists were ranking this toothpaste brand over other toothpaste brands, instead of against not brushing your teeth at all; this makes the statistic misleading.

Another way statistics can be misleading is in the sample size that the data was collected in. For example, let's say you found a statistic that says 4 out of 5 women prefer wearing high heels over flats to work. However, when you start looking closer at the source the statistic came from, you find that this statistic came from someone asking 5 women they work with in a corporate law firm if they liked wearing heels or flats to work. This is a problem for several reasons.

First, the information was collected from a very small sample size (5 women who all work at the same place). These 5 women cannot represent all women and their opinions on high heels. Second, this sample is very biased, because all of the women work in the same corporate law firm. These women's opinions are not going to reflect all women's opinions, regardless of the number of women sampled, because the women are too similar to one another. If all women in all industries were surveyed for this question, the statistic would look very different. Because of this, it's always important to know the context of any statistic before you use it in your argument. Similarly, you want to be wary of statistics you find that don't have context or can't be tracked back to an original source.

Just like evaluating the credibility of your sources , you will want to do the same for when you want to use statistics in your research. Ask yourself the following questions:

• Can you find the original source that this statistic was published in? This will help you understand the context of the statistics.
• Who published the original source and where was it published?
• Who collected the information for the statistics? Do they have any kind of agenda/stake in the statistics?
• When was the information collected? Could it be out of date?
• How big was the sample size/how much data was collected? What were the demographics of the sample size? This will help you figure out if the statistics are representative of a certain group or area. 

Here is an article that goes deeper into how statistics can be misleading and ways to determine whether your statistics are misleading or not.

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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

Blaise Agüera y Arcas

How photosynth can connect the world's images.

Chris Jordan

Turning powerful stats into art.

The birth of a word

Hans Rosling

The magic washing machine.

Nathalie Miebach

Art made of storms.

• Why Study Statistics?
• Descriptive & Inferential Statistics
• Fundamental Elements of Statistics
• Quantitative and Qualitative Data
• Measurement Data Levels
• Collecting Data
• Ethics in Statistics
• Describing Qualitative Data
• Describing Quantitative Data
• Stem-and-Leaf Plots
• Measures of Central Tendency
• Measures of Variability
• Describing Data using the Mean and Standard Deviation
• Measures of Position
• Counting Techniques
• Simple & Compound Events
• Independent and Dependent Events
• Mutually Exclusive and Non-Mutually Exclusive Events
• Permutations and Combinations
• Normal Distribution
• Central Limit Theorem
• Confidence Intervals
• Determining the Sample Size
• Hypothesis Testing
• Hypothesis Testing Process

The field of statistics is the science of learning from data. Statistical knowledge helps you use the proper methods to collect the data, employ the correct analyses, and effectively present the results. Statistics is a crucial process behind how we make discoveries in science, make decisions based on data, and make predictions. Statistics allows you to understand a subject much more deeply.

Statistics is an exciting field about the thrill of discovery, learning, and challenging your assumptions. Statistics facilitates the creation of new knowledge. Bit by bit, we push back the frontier of what is known. 

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• Next: Descriptive & Inferential Statistics >>
• Last Updated: Apr 20, 2023 12:47 PM
• URL: https://libraryguides.centennialcollege.ca/c.php?g=717168

What this handout is about

This handout will provide a broad overview of gathering and using evidence. It will help you decide what counts as evidence, put evidence to work in your writing, and determine whether you have enough evidence. It will also offer links to additional resources.

Introduction

Many papers that you write in college will require you to make an argument ; this means that you must take a position on the subject you are discussing and support that position with evidence. It’s important that you use the right kind of evidence, that you use it effectively, and that you have an appropriate amount of it. If, for example, your philosophy professor didn’t like it that you used a survey of public opinion as your primary evidence in your ethics paper, you need to find out more about what philosophers count as good evidence. If your instructor has told you that you need more analysis, suggested that you’re “just listing” points or giving a “laundry list,” or asked you how certain points are related to your argument, it may mean that you can do more to fully incorporate your evidence into your argument. Comments like “for example?,” “proof?,” “go deeper,” or “expand” in the margins of your graded paper suggest that you may need more evidence. Let’s take a look at each of these issues—understanding what counts as evidence, using evidence in your argument, and deciding whether you need more evidence.

What counts as evidence?

Before you begin gathering information for possible use as evidence in your argument, you need to be sure that you understand the purpose of your assignment. If you are working on a project for a class, look carefully at the assignment prompt. It may give you clues about what sorts of evidence you will need. Does the instructor mention any particular books you should use in writing your paper or the names of any authors who have written about your topic? How long should your paper be (longer works may require more, or more varied, evidence)? What themes or topics come up in the text of the prompt? Our handout on understanding writing assignments can help you interpret your assignment. It’s also a good idea to think over what has been said about the assignment in class and to talk with your instructor if you need clarification or guidance.

What matters to instructors?

Instructors in different academic fields expect different kinds of arguments and evidence—your chemistry paper might include graphs, charts, statistics, and other quantitative data as evidence, whereas your English paper might include passages from a novel, examples of recurring symbols, or discussions of characterization in the novel. Consider what kinds of sources and evidence you have seen in course readings and lectures. You may wish to see whether the Writing Center has a handout regarding the specific academic field you’re working in—for example, literature , sociology , or history .

What are primary and secondary sources?

A note on terminology: many researchers distinguish between primary and secondary sources of evidence (in this case, “primary” means “first” or “original,” not “most important”). Primary sources include original documents, photographs, interviews, and so forth. Secondary sources present information that has already been processed or interpreted by someone else. For example, if you are writing a paper about the movie “The Matrix,” the movie itself, an interview with the director, and production photos could serve as primary sources of evidence. A movie review from a magazine or a collection of essays about the film would be secondary sources. Depending on the context, the same item could be either a primary or a secondary source: if I am writing about people’s relationships with animals, a collection of stories about animals might be a secondary source; if I am writing about how editors gather diverse stories into collections, the same book might now function as a primary source.

Where can I find evidence?

Here are some examples of sources of information and tips about how to use them in gathering evidence. Ask your instructor if you aren’t sure whether a certain source would be appropriate for your paper.

Print and electronic sources

Books, journals, websites, newspapers, magazines, and documentary films are some of the most common sources of evidence for academic writing. Our handout on evaluating print sources will help you choose your print sources wisely, and the library has a tutorial on evaluating both print sources and websites. A librarian can help you find sources that are appropriate for the type of assignment you are completing. Just visit the reference desk at Davis or the Undergraduate Library or chat with a librarian online (the library’s IM screen name is undergradref).

Observation

Sometimes you can directly observe the thing you are interested in, by watching, listening to, touching, tasting, or smelling it. For example, if you were asked to write about Mozart’s music, you could listen to it; if your topic was how businesses attract traffic, you might go and look at window displays at the mall.

An interview is a good way to collect information that you can’t find through any other type of research. An interview can provide an expert’s opinion, biographical or first-hand experiences, and suggestions for further research.

Surveys allow you to find out some of what a group of people thinks about a topic. Designing an effective survey and interpreting the data you get can be challenging, so it’s a good idea to check with your instructor before creating or administering a survey.

Experiments

Experimental data serve as the primary form of scientific evidence. For scientific experiments, you should follow the specific guidelines of the discipline you are studying. For writing in other fields, more informal experiments might be acceptable as evidence. For example, if you want to prove that food choices in a cafeteria are affected by gender norms, you might ask classmates to undermine those norms on purpose and observe how others react. What would happen if a football player were eating dinner with his teammates and he brought a small salad and diet drink to the table, all the while murmuring about his waistline and wondering how many fat grams the salad dressing contained?

Personal experience

Using your own experiences can be a powerful way to appeal to your readers. You should, however, use personal experience only when it is appropriate to your topic, your writing goals, and your audience. Personal experience should not be your only form of evidence in most papers, and some disciplines frown on using personal experience at all. For example, a story about the microscope you received as a Christmas gift when you were nine years old is probably not applicable to your biology lab report.

Using evidence in an argument

Does evidence speak for itself.

Absolutely not. After you introduce evidence into your writing, you must say why and how this evidence supports your argument. In other words, you have to explain the significance of the evidence and its function in your paper. What turns a fact or piece of information into evidence is the connection it has with a larger claim or argument: evidence is always evidence for or against something, and you have to make that link clear.

As writers, we sometimes assume that our readers already know what we are talking about; we may be wary of elaborating too much because we think the point is obvious. But readers can’t read our minds: although they may be familiar with many of the ideas we are discussing, they don’t know what we are trying to do with those ideas unless we indicate it through explanations, organization, transitions, and so forth. Try to spell out the connections that you were making in your mind when you chose your evidence, decided where to place it in your paper, and drew conclusions based on it. Remember, you can always cut prose from your paper later if you decide that you are stating the obvious.

Here are some questions you can ask yourself about a particular bit of evidence:

• OK, I’ve just stated this point, but so what? Why is it interesting? Why should anyone care?
• What does this information imply?
• What are the consequences of thinking this way or looking at a problem this way?
• I’ve just described what something is like or how I see it, but why is it like that?
• I’ve just said that something happens—so how does it happen? How does it come to be the way it is?
• Why is this information important? Why does it matter?
• How is this idea related to my thesis? What connections exist between them? Does it support my thesis? If so, how does it do that?
• Can I give an example to illustrate this point?

Answering these questions may help you explain how your evidence is related to your overall argument.

How can I incorporate evidence into my paper?

There are many ways to present your evidence. Often, your evidence will be included as text in the body of your paper, as a quotation, paraphrase, or summary. Sometimes you might include graphs, charts, or tables; excerpts from an interview; or photographs or illustrations with accompanying captions.

When you quote, you are reproducing another writer’s words exactly as they appear on the page. Here are some tips to help you decide when to use quotations:

• Quote if you can’t say it any better and the author’s words are particularly brilliant, witty, edgy, distinctive, a good illustration of a point you’re making, or otherwise interesting.
• Quote if you are using a particularly authoritative source and you need the author’s expertise to back up your point.
• Quote if you are analyzing diction, tone, or a writer’s use of a specific word or phrase.
• Quote if you are taking a position that relies on the reader’s understanding exactly what another writer says about the topic.

Be sure to introduce each quotation you use, and always cite your sources. See our handout on quotations for more details on when to quote and how to format quotations.

Like all pieces of evidence, a quotation can’t speak for itself. If you end a paragraph with a quotation, that may be a sign that you have neglected to discuss the importance of the quotation in terms of your argument. It’s important to avoid “plop quotations,” that is, quotations that are just dropped into your paper without any introduction, discussion, or follow-up.

Paraphrasing

When you paraphrase, you take a specific section of a text and put it into your own words. Putting it into your own words doesn’t mean just changing or rearranging a few of the author’s words: to paraphrase well and avoid plagiarism, try setting your source aside and restating the sentence or paragraph you have just read, as though you were describing it to another person. Paraphrasing is different than summary because a paraphrase focuses on a particular, fairly short bit of text (like a phrase, sentence, or paragraph). You’ll need to indicate when you are paraphrasing someone else’s text by citing your source correctly, just as you would with a quotation.

When might you want to paraphrase?

• Paraphrase when you want to introduce a writer’s position, but their original words aren’t special enough to quote.
• Paraphrase when you are supporting a particular point and need to draw on a certain place in a text that supports your point—for example, when one paragraph in a source is especially relevant.
• Paraphrase when you want to present a writer’s view on a topic that differs from your position or that of another writer; you can then refute writer’s specific points in your own words after you paraphrase.
• Paraphrase when you want to comment on a particular example that another writer uses.
• Paraphrase when you need to present information that’s unlikely to be questioned.

When you summarize, you are offering an overview of an entire text, or at least a lengthy section of a text. Summary is useful when you are providing background information, grounding your own argument, or mentioning a source as a counter-argument. A summary is less nuanced than paraphrased material. It can be the most effective way to incorporate a large number of sources when you don’t have a lot of space. When you are summarizing someone else’s argument or ideas, be sure this is clear to the reader and cite your source appropriately.

Statistics, data, charts, graphs, photographs, illustrations

Sometimes the best evidence for your argument is a hard fact or visual representation of a fact. This type of evidence can be a solid backbone for your argument, but you still need to create context for your reader and draw the connections you want them to make. Remember that statistics, data, charts, graph, photographs, and illustrations are all open to interpretation. Guide the reader through the interpretation process. Again, always, cite the origin of your evidence if you didn’t produce the material you are using yourself.

Do I need more evidence?

Let’s say that you’ve identified some appropriate sources, found some evidence, explained to the reader how it fits into your overall argument, incorporated it into your draft effectively, and cited your sources. How do you tell whether you’ve got enough evidence and whether it’s working well in the service of a strong argument or analysis? Here are some techniques you can use to review your draft and assess your use of evidence.

Make a reverse outline

A reverse outline is a great technique for helping you see how each paragraph contributes to proving your thesis. When you make a reverse outline, you record the main ideas in each paragraph in a shorter (outline-like) form so that you can see at a glance what is in your paper. The reverse outline is helpful in at least three ways. First, it lets you see where you have dealt with too many topics in one paragraph (in general, you should have one main idea per paragraph). Second, the reverse outline can help you see where you need more evidence to prove your point or more analysis of that evidence. Third, the reverse outline can help you write your topic sentences: once you have decided what you want each paragraph to be about, you can write topic sentences that explain the topics of the paragraphs and state the relationship of each topic to the overall thesis of the paper.

For tips on making a reverse outline, see our handout on organization .

Color code your paper

You will need three highlighters or colored pencils for this exercise. Use one color to highlight general assertions. These will typically be the topic sentences in your paper. Next, use another color to highlight the specific evidence you provide for each assertion (including quotations, paraphrased or summarized material, statistics, examples, and your own ideas). Lastly, use another color to highlight analysis of your evidence. Which assertions are key to your overall argument? Which ones are especially contestable? How much evidence do you have for each assertion? How much analysis? In general, you should have at least as much analysis as you do evidence, or your paper runs the risk of being more summary than argument. The more controversial an assertion is, the more evidence you may need to provide in order to persuade your reader.

Play devil’s advocate, act like a child, or doubt everything

This technique may be easiest to use with a partner. Ask your friend to take on one of the roles above, then read your paper aloud to them. After each section, pause and let your friend interrogate you. If your friend is playing devil’s advocate, they will always take the opposing viewpoint and force you to keep defending yourself. If your friend is acting like a child, they will question every sentence, even seemingly self-explanatory ones. If your friend is a doubter, they won’t believe anything you say. Justifying your position verbally or explaining yourself will force you to strengthen the evidence in your paper. If you already have enough evidence but haven’t connected it clearly enough to your main argument, explaining to your friend how the evidence is relevant or what it proves may help you to do so.

Common questions and additional resources

• I have a general topic in mind; how can I develop it so I’ll know what evidence I need? And how can I get ideas for more evidence? See our handout on brainstorming .
• Who can help me find evidence on my topic? Check out UNC Libraries .
• I’m writing for a specific purpose; how can I tell what kind of evidence my audience wants? See our handouts on audience , writing for specific disciplines , and particular writing assignments .
• How should I read materials to gather evidence? See our handout on reading to write .
• How can I make a good argument? Check out our handouts on argument and thesis statements .
• How do I tell if my paragraphs and my paper are well-organized? Review our handouts on paragraph development , transitions , and reorganizing drafts .
• How do I quote my sources and incorporate those quotes into my text? Our handouts on quotations and avoiding plagiarism offer useful tips.
• How do I cite my evidence? See the UNC Libraries citation tutorial .
• I think that I’m giving evidence, but my instructor says I’m using too much summary. How can I tell? Check out our handout on using summary wisely.
• I want to use personal experience as evidence, but can I say “I”? We have a handout on when to use “I.”

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

Lunsford, Andrea A., and John J. Ruszkiewicz. 2016. Everything’s an Argument , 7th ed. Boston: Bedford/St Martin’s.

Miller, Richard E., and Kurt Spellmeyer. 2016. The New Humanities Reader , 5th ed. Boston: Cengage.

University of Maryland. 2019. “Research Using Primary Sources.” Research Guides. Last updated October 28, 2019. https://lib.guides.umd.edu/researchusingprimarysources .

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, Its Importance and Application Essay

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Importance of Statistics

Examples of how statistics can be used.

Statistics is a science that helps businesses in decision-making. It entails the collection of data, tabulation, and inference making. In essence, Statistics is widely used in businesses to make forecasts, research on the market conditions, and ensure the quality of products. The importance of statistics is to determine the type of data required, how it is collected, and the way it is analyzed to get factual answers.

Statistics is the collection of numerical facts and figures on such things as population, education, economy, incomes, etc. Figures collected are referred to as data. The collection, analysis, and interpretation of data are referred to as statistical methods (Lind, Marchal, & Wathen, 2011).

Two subdivisions of the statistical method are:

• Descriptive statistics: Deals with compilation and presentation of data in various forms such as tables, graphs, and diagrams from which conclusions can be drawn and decisions made. Businesses, for example, use descriptive statistics when presenting their annual accounts and reports.
• Mathematical/inferential/inductive statistics: This deals with the tools of statistics. These are the techniques that are used to analyze, make estimates, inferences, and conclude the data collected (McClave, Benson, & Sincish, 2011).

Statistics have been collected since the earliest times in history. Rulers needed to have data on population and wealth so that taxes could be levied to maintain the state and the courts. Details on the composition of the population were necessary to determine the strength of the nation. With the growth of the population and the advent of the industrial revolution in the 18 th and 19 th centuries, there was a need for greater volumes of statistics in an increasing variety of subjects such as production, expenditure, incomes, imports, and exports. In the 19 th and 20 th centuries, governments worldwide took more control in economic activities such as education and health. This led to the enormous expansion of statistics collected by governments (Lind, Marchal, & Wathen, 2011).

The government’s economic activities have expanded in the last three centuries and so have the companies/businesses grown, as well. Indeed, some have grown to such an extent that their annual turnover is greater than the annual budgets of some governments. Big firms have to make decisions based on data. The companies collect data on their own other than these sources to establish:

• Competition
• Customer needs
• Production and personnel costs
• Accounting reports on liabilities, assets, losses, and income

The tools of statistics are important for companies in areas such as planning, forecasting, and quality control (McClave, Benson, & Sincish, 2011).

To Ensure Quality

A continuous check into quality using programs is very helpful in ensuring that only quality products come out of production firms. This, in turn, ensures that there is minimum wastage or errors in the production of goods and services (McClave, Benson, & Sincish, 2011).

Making Connections

Statistics are good in revealing relationships between variables – a good example is when a company makes a close relationship between the numbers of dissatisfied customers and the turnover. Indeed, there is an inverse relationship between the number of dissatisfied customers and turnover.

Backing Judgment

With only a small sample of the population studied, the management can come up with a concrete understanding of how the customers will relate to their products. This, therefore, will help them decide on whether to or not continue with that line of production (Lind, Marchal, & Wathen, 2011).

Lind, D., Marchal, G., & Wathen, A. (2011). Basic statistics for business and economics (7 th ed.). New York, NY: McGraw-Hill/Irwin.

McClave, T., Benson, G., & Sincish, T. (2011). Statistics for business and economics (11 th ed.). Boston, MA: Pearson-Prentice Hall.

• Descriptive and Inferential Statistical Tests
• Essentials of Statistics for the Behavioural Sciences
• Descriptive Statistics and Probability
• Descriptive Statistics in Nursing
• Descriptive Statistics Method: Household Income Analysis
• Hypothesis Testing in Practical Statistics
• Applied Statistics for Healthcare Professionals
• Time Series and Causal Models in Forecasting
• Study Hours and Grades in Educational Institutions
• The Repeated-Measures ANOVA in a General Context
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2020, October 1). Statistics, Its Importance and Application. https://ivypanda.com/essays/statistics-its-importance-and-application/

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IvyPanda . 2020. "Statistics, Its Importance and Application." October 1, 2020. https://ivypanda.com/essays/statistics-its-importance-and-application/.

1. IvyPanda . "Statistics, Its Importance and Application." October 1, 2020. https://ivypanda.com/essays/statistics-its-importance-and-application/.

Bibliography

IvyPanda . "Statistics, Its Importance and Application." October 1, 2020. https://ivypanda.com/essays/statistics-its-importance-and-application/.

Essay on Statistics: Meaning and Definition of Statistics

“Statistics”, that a word is often used, has been derived from the Latin word ‘Status’ that means a group of numbers or figures; those represent some information of our human interest.

We find statistics in everyday life, such as in books or other information papers or TV or newspapers.

Although, in the beginning it was used by Kings only for collecting information about states and other information which was needed about their people, their number, revenue of the state etc.

This was known as the science of the state because it was used only by the Kings. So it got its development as ‘Kings’ subject or ‘Science of Kings’ or we may call it as “Political Arithmetic’s”. It was for the first time, perhaps in Egypt to conduct census of population in 3050 B.C. because the king needed money to erect pyramids. But in India, it is thought, that, it started dating back to Chandra Gupta Maurya’s kingdom under Chankya to collect the data of births and deaths. TM has also been stated in Chankya’s Arthshastra.

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But now-a-days due to its pervading nature, its scope has increased and widened. It is now used in almost in all the fields of human knowledge and skills like Business, Commerce, Economics, Social Sciences, Politics, Planning, Medicine and other sciences, Physical as well as Natural.

Definition :

The term ‘Statistics’ has been defined in two senses, i.e. in Singular and in Plural sense.

“Statistics has two meanings, as in plural sense and in singular sense”.

—Oxford Dictionary

In plural sense, it means a systematic collection of numerical facts and in singular sense; it is the science of collecting, classifying and using statistics.

A. In the Plural Sense :

“Statistics are numerical statements of facts in any department of enquiry placed in relation to each other.” —A.L. Bowley

“The classified facts respecting the condition of the people in a state—especially those facts which can be stated in numbers or in tables of numbers or in any tabular or classified arrangement.” —Webster

These definitions given above give a narrow meaning to the statistics as they do not indicate its various aspects as are witnessed in its practical applications. From the this point of view the definition given by Prof. Horace Sacrist appears to be the most comprehensive and meaningful:

“By statistics we mean aggregates of facts affected to a marked extent by multiplicity of causes, numerically expressed, enumerated or estimated according to reasonable standard of accuracy, collected in a systematic manner for a predetermined purpose, and placed in relation to each other.”—Horace Sacrist

B. In the Singular Sense :

“Statistics refers to the body of technique or methodology, which has been developed for the collection, presentation and analysis of quantitative data and for the use of such data in decision making.” —Ncttor and Washerman

“Statistics may rightly be called the science of averages.” —Bowleg

“Statistics may be defined as the collection, presentation, analysis, and interpretation of numerical data.” —Croxton and Cowden

Stages of Investigations :

1. Collection of Data:

It is the first stage of investigation and is regarding collection of data. It is determined that which method of collection is needed in this problem and then data are collected.

2. Organisation of Data:

It is second stage. The data are simplified and made comparative and are classified according to time and place.

3. Presentation of Data:

In this third stage, organised data are made simple and attractive. These are presented in the form of tables diagrams and graphs.

4. Analysis of Data:

Forth stage of investigation is analysis. To get correct results, analysis is necessary. It is often undertaken using Measures of central tendencies, Measures of dispersion, correlation, regression and interpolation etc.

5. Interpretation of Data:

In this last stage, conclusions are enacted. Use of comparisons is made. On this basis, forecasting is made.

Some Modern Definitions :

From the above two senses of statistics, modem definitions have emerged as given below:

“Statistics is a body of methods for making wise decisions on the face of uncertainty.” —Wallis and Roberts

“Statistics is a body of methods for obtaining and analyzing numerical data in order to make better decisions in an uncertain world.” —Edward N. Dubois

So, from above definitions we find that science of statistics also includes the methods of collecting, organising, presenting, analysing and interpreting numerical facts and decisions are taken on their basis.

The most proper definition of statistics can be given as following after analysing the various definitions of statistics.

“Statistics in the plural sense are numerical statements of facts capable of some meaningful analysis and interpretation, and in singular sense, it relates to the collection, classification, presentation and interpretation of numerical data.”

Related Articles:

• 7 Main Characteristics of Statistics – Explained!
• Use of Statistics in Economics: Origin, Meaning and Other Details
• Nature and Subject Matter of Statistics
• Relation of Statistics with other Sciences

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Just How Demure Are You?

A TikTok trend has breathed new life into a fusty word.

By The Styles Desk

If you opened TikTok this week and felt that suddenly everyone was using the word “demure” out of nowhere, you’re not alone.

Your morning coffee with just a little half-and-half? Demure. The way you sit down gracefully on the subway? Very demure. The way you floss your teeth after lunch in the office bathroom before returning to your cubicle? Absolutely, totally demure.

Seemingly overnight, an adjective usually reserved for a reserved woman has become the semi-ironic word du jour on social media.

On Aug. 2, a TikTok creator who goes by the name Jools Lebron posted a video with tips about managing makeup and mustache sweat and being demure.

Later that day, she posted another video , which has been viewed four million times, offering tips on how to be demure at work. “Very demure, very mindful," she says, explaining her perfume, clothing and hairstyle choices for the workplace. Ms. Lebron has since posted dozens more such videos discussing how to be demure in all sorts of situations, like nail salons, hotels and drag shows.

Ms. Lebron, who did not respond to requests for comment, is one of several trans creators on TikTok whose playful use of “demure” in recent videos has helped the word catch on with other users.

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MIT study explains why laws are written in an incomprehensible style

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Legal documents are notoriously difficult to understand, even for lawyers. This raises the question: Why are these documents written in a style that makes them so impenetrable?

MIT cognitive scientists believe they have uncovered the answer to that question. Just as “magic spells” use special rhymes and archaic terms to signal their power, the convoluted language of legalese acts to convey a sense of authority, they conclude.

In a study appearing this week in the journal of the Proceedings of the National Academy of Sciences , the researchers found that even non-lawyers use this type of language when asked to write laws.

“People seem to understand that there’s an implicit rule that this is how laws should sound, and they write them that way,” says Edward Gibson, an MIT professor of brain and cognitive sciences and the senior author of the study.

Eric Martinez PhD ’24 is the lead author of the study. Francis Mollica, a lecturer at the University of Melbourne, is also an author of the paper .

Casting a legal spell

Gibson’s research group has been studying the unique characteristics of legalese since 2020, when Martinez came to MIT after earning a law degree from Harvard Law School. In a 2022 study , Gibson, Martinez, and Mollica analyzed legal contracts totaling about 3.5 million words, comparing them with other types of writing, including movie scripts, newspaper articles, and academic papers.

That analysis revealed that legal documents frequently have long definitions inserted in the middle of sentences — a feature known as “center-embedding.” Linguists have previously found that this kind of structure can make text much more difficult to understand.

“Legalese somehow has developed this tendency to put structures inside other structures, in a way which is not typical of human languages,” Gibson says.

In a follow-up study published in 2023, the researchers found that legalese also makes documents more difficult for lawyers to understand. Lawyers tended to prefer plain English versions of documents, and they rated those versions to be just as enforceable as traditional legal documents.

“Lawyers also find legalese to be unwieldy and complicated,” Gibson says. “Lawyers don’t like it, laypeople don’t like it, so the point of this current paper was to try and figure out why they write documents this way.”

The researchers had a couple of hypotheses for why legalese is so prevalent. One was the “copy and edit hypothesis,” which suggests that legal documents begin with a simple premise, and then additional information and definitions are inserted into already existing sentences, creating complex center-embedded clauses.

“We thought it was plausible that what happens is you start with an initial draft that’s simple, and then later you think of all these other conditions that you want to include. And the idea is that once you’ve started, it’s much easier to center-embed that into the existing provision,” says Martinez, who is now a fellow and instructor at the University of Chicago Law School.

However, the findings ended up pointing toward a different hypothesis, the so-called “magic spell hypothesis.” Just as magic spells are written with a distinctive style that sets them apart from everyday language, the convoluted style of legal language appears to signal a special kind of authority, the researchers say.

“In English culture, if you want to write something that’s a magic spell, people know that the way to do that is you put a lot of old-fashioned rhymes in there. We think maybe center-embedding is signaling legalese in the same way,” Gibson says.

In this study, the researchers asked about 200 non-lawyers (native speakers of English living in the United States, who were recruited through a crowdsourcing site called Prolific), to write two types of texts. In the first task, people were told to write laws prohibiting crimes such as drunk driving, burglary, arson, and drug trafficking. In the second task, they were asked to write stories about those crimes.

To test the copy and edit hypothesis, half of the participants were asked to add additional information after they wrote their initial law or story. The researchers found that all of the subjects wrote laws with center-embedded clauses, regardless of whether they wrote the law all at once or were told to write a draft and then add to it later. And, when they wrote stories related to those laws, they wrote in much plainer English, regardless of whether they had to add information later.

“When writing laws, they did a lot of center-embedding regardless of whether or not they had to edit it or write it from scratch. And in that narrative text, they did not use center-embedding in either case,” Martinez says.

In another set of experiments, about 80 participants were asked to write laws, as well as descriptions that would explain those laws to visitors from another country. In these experiments, participants again used center-embedding for their laws, but not for the descriptions of those laws.

The origins of legalese

Gibson’s lab is now investigating the origins of center-embedding in legal documents. Early American laws were based on British law, so the researchers plan to analyze British laws to see if they feature the same kind of grammatical construction. And going back much farther, they plan to analyze whether center-embedding is found in the Hammurabi Code, the earliest known set of laws, which dates to around 1750 BC.

“There may be just a stylistic way of writing from back then, and if it was seen as successful, people would use that style in other languages,” Gibson says. “I would guess that it’s an accidental property of how the laws were written the first time, but we don’t know that yet.”

The researchers hope that their work, which has identified specific aspects of legal language that make it more difficult to understand, will motivate lawmakers to try to make laws more comprehensible. Efforts to write legal documents in plainer language date to at least the 1970s, when President Richard Nixon declared that federal regulations should be written in “layman’s terms.” However, legal language has changed very little since that time.

“We have learned only very recently what it is that makes legal language so complicated, and therefore I am optimistic about being able to change it,” Gibson says. 

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Researchers at MIT have found that the use of legalese in writing “to assert authority over those less versed in such language,” reports Noor Al-Sibai for Futurism . “By studying this cryptic take on the English language, the researchers are hoping to make legal documents much easier to read in the future,” explains Al-Sibai.

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Canada's inflation rate dips to 2.5% in July, lowest since March 2021

Passenger vehicles, electricity see decline in inflation, statistics canada says.

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Canada's annual inflation rate dropped to 2.5 per cent in July  — down from 2.7 per cent in June, Statistics Canada said Tuesday.

The national statistics agency said inflation increased at the slowest pace in more than three years, since March 2021.

The deceleration was attributed in part to lower prices for travel as compared to a year earlier, when travel tours, airline tickets and accommodation soared in the first summer without COVID-19 restrictions.

Passenger vehicles and electricity also saw a year-over-year decrease in price. 

The cost of groceries rose at a rate of 2.1 per cent, while shelter rose by 5.7 per cent. Rent costs rose by 8.5 per cent year over year, while mortgage interest was 21 per cent higher amid the hikes in interest rates that began in early 2022.

Prime Minister Justin Trudeau welcomed the news in a post on social media.

"We've still got a lot more work to do to make sure Canadians feel that relief in their bank accounts. But inflation is cooling, and that's welcome news," he wrote on X.

Challenges persist

David MacDonald, economist with the Canadian Centre for Policy Alternatives, pointed out that the high cost of things like groceries persists, despite a slowdown in how much they have increased since last summer.

This is what's known by economists as the base-effect, which refers to the impact of price movements from a year ago on the calculation of the year-over-year inflation rate.

"For regular folks, they still matter because prices aren't decreasing. It's just those increases happened more than a year ago," he said. 

Another Statistics Canada report earlier this week drove home the challenge of making ends meet for many Canadians.

The report found that, adjusted for an annual rate of inflation of 6.8 per cent, the 2022 median family after-tax income was four per cent lower than it was in 2021.

Statistics Canada said lone-parent families in which the parent was under 25 saw the largest decrease, falling 15.1 per cent in constant dollars to $24,690 in 2022.

Experts expect another rate cut

Overall, price pressures in Canada have steadily eased this year, bringing the annual inflation rate below three per cent since January.

The Bank of Canada has been encouraged by this progress and lowered its key interest rate at its last two decision meetings.

Governor Tiff Macklem has signalled more rate cuts are coming, as long as inflation continues to slow.

At the last interest rate announcement, Macklem said the governing council decided to lower its policy rate, in part to help the economy pick up speed again.   

'Why do we care about core inflation?' Bank of Canada governor explains

Its key interest rate now stands at 4.5 per cent.

Analysts said the latest inflation numbers pave the way for more rate cuts.

"Overall, the Bank of Canada will welcome today's report as it confirms that inflation continues to ease. Moreover, the breadth of inflationary pressures and the momentum of core inflation suggest further progress," said Charles St-Arnaud, chief economist at Alberta Central.

TD Bank senior economist James Orlando said there's "nothing stopping the bank from cutting rates by another 25 basis points."

"With inflation risks fading, the central bank's focus has pivoted to weakness in the rest of the economy," he said.

The central bank is scheduled to make its next interest rate announcement on Sept. 4.

With files from Anis Heydari and The Canadian Press

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• Home Office

Why do people come to the UK? To work

Published 22 August 2024

© Crown copyright 2024

This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] .

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This publication is available at https://www.gov.uk/government/statistics/immigration-system-statistics-year-ending-june-2024/why-do-people-come-to-the-uk-to-work

Back to ‘Immigration system statistics, year ending June 2024’ contents page .

Data relates to the year ending June 2024 and all comparisons are with the year ending June 2023 (unless indicated otherwise). All data includes dependants, unless indicated otherwise.

1. Work-related visas

Between 2009 and 2019 the number of work visas granted were relatively stable. The COVID-19 pandemic restricted global travel throughout 2020 and parts of 2021, leading to a temporary fall in visa grants. On 31 January 2020 the UK left the European Union ( EU ) and following a ‘transition period’, which came to an end on 31 December 2020, made a number of changes to the UK Immigration system and at the same time ended freedom of movement between the UK and EU countries. These various changes led to a change in immigration for work.

There were 286,382 visas granted to main applicants in all work categories in the year ending June 2024, 11% fewer than the previous year, but more than double (+109%) 2019 levels.

Since the beginning of 2021, there has been an increase in the number of work visas granted, with ‘Health and Care Worker’ visa numbers in particular growing sharply between year ending September 2022 and year ending September 2023, following the expansion of the route in February 2022 to boost the social care workforce. The growth in this route was accompanied by a large increase in the number of work-related dependant visas granted, with the majority of all work dependants (62%) over the latest 3 years being on the ‘Health and Care Worker’ visa.

In December 2023, the Home Office announced a set of policy changes , impacting who can apply for work visas, which came into effect at various points in 2024. The number of ‘Health and Care Worker’ visas issued to main applicants has fallen in the most recent quarter – down 81% between April and June 2024 compared with the same period in 2023. Dependant visas have also fallen by 66% over the same period.

Other skilled work routes and ‘Temporary Worker’ visas also increased from 2021, but their numbers have levelled off in the last 2 years.

Figure 1: Work visas granted to main applicants between the year ending June 2014 and the year ending June 2024

Source: Entry clearance visa applications and outcomes – Vis_D02

• The ‘Worker’ visa line divides after 2021 (denoted by the dashed lines), when the ‘Health and Care’ visa was introduced alongside other skilled work. Prior to this, the ‘Worker’ category is comprised of visas under the previous ‘Tier 2’ points-based system.

1.1 Health and Care Workers, other skilled work routes, and their family members

There were 89,095 ‘Health and Care Worker’ visas granted to main applicants in the year ending June 2024, a 26% decrease compared to the previous year.

The increase in ‘Health and Care Worker’ visas in the 2 years between 2021 and 2023 (as shown in Figure 1 ), was due to more care workers and home carers coming to the UK, which peaked at 88,814 in the year ending December 2023. However, the number of care workers and home carers issued visas have fallen since the latter part of 2023. The fall in applications and grants towards the end of 2023 is likely due to more scrutiny applied by the Home Office to employers in the health and social care sector, and compliance activity taken against employers of migrant workers, as well as the recent policy measures affecting care workers introduced in March and April of this year. The number of grants for ‘Health and Care Worker’ main applicants fell by 81% to 6,564 grants between April and June 2024, compared with the same period in 2023 when there were 35,470 grants.

The number of grants to main applicants on other routes in the ‘Worker’ category (which includes ‘Skilled Worker’ visas) has increased by 79% since 2021, but in the latest year has fallen by 3%.

Figure 2: Visas granted to dependants of work visa holders by visa route, between the year ending June 2019 and the year ending June 2024

• The ‘Skilled Worker’ and ‘Health and Care Worker’ visas were introduced in 2020 and together replaced the ‘Tier 2 – General’ visa.

Figure 2 shows that the introduction of the ‘Health and Care’ and ‘Skilled Worker’ routes contributed to a large overall increase in visas issued to work dependants, from 53,260 in year ending June 2019 to 260,392 in year ending June 2024, with Health and Care’ dependants alone accounting for 69% of all work dependants in the latest year.

In the year ending June 2024, visas issued to dependants on the ‘Health and Care’ route were higher than visas issued to main applicants, with approximately 2 dependants per main applicant. By contrast, those on ‘Skilled Worker’ visas brought less than one dependant per main applicant. However, the number of ‘Health and Care’ dependants in the second quarter of 2024 was 15,198, 68% lower than the first quarter of 2024. This decrease in grants is likely influenced by falls in grants to main applicants since the third quarter of 2023, and the recent policy change for care worker dependants .

1.2 Temporary workers

The number of ‘Temporary Worker’ visas granted to main applicants in the year ending June 2024 was 77,419. This is slightly higher (+3%) than the previous year but almost double (+90%) the number in the year ending June 2019 (40,702), due to the growth in the ‘Seasonal Worker’ visa route.

In the year ending June 2024, 44% of ‘Temporary Worker’ visas granted to main applicants were for seasonal work, 31% were under the ‘Youth Mobility Scheme’ and the remaining 25% were for other temporary work.

Figure 3: ‘Temporary Worker’ visas granted to main applicants between the year ending June 2019 and the year ending June 2024

Figure 3 shows grants of ‘Seasonal Worker’ visas in the year ending June 2024 increased by 5% to 34,332 compared with the previous year. The ‘Seasonal Worker’ route was launched in March 2019 with the number of visas that could be granted limited to 2,500 per year. The annual quota has since increased to 47,000 for 2024, which partly explains the overall growth in ‘Temporary Worker’ visas issued over the last few years.

Whilst ‘Seasonal Worker’ visas have grown slightly in the latest year, the nationalities of applicants have changed. Central Asian nationalities (such as Kyrgyzstan, Tajikistan, Uzbekistan and Kazakhstan) have together grown by 23% to 26,230 and represent three-quarters (76%) of grants in the latest year. Ukrainian nationals, in contrast have fallen by 62% to 1,208, following the start of the war in Ukraine and the launch of the Ukraine visa schemes in 2022.

The number of ‘Youth Mobility Scheme’ visas issued increased by 6% to 24,091 in the year ending June 2024 compared with the previous year. The rise in Youth Mobility since the year ending June 2021 could reflect a bounce-back for the scheme which was heavily impacted by travel restrictions during the pandemic, but recent numbers also include the addition of the India Young Professionals Scheme , which held its first ballot in February 2023. The route has a quota of 3,000 places each year, and in the year ending June 2024, there were 2,234 grants to Indian nationals under this scheme.

2. Work-related extensions

In the year ending June 2024, grants of an extension into work routes for main applicants increased by 38% compared to the previous year, to 378,732, primarily driven by extensions in the ‘Health and Care Worker’, ‘Skilled Worker’ and ‘Graduate’ work routes.

Analysis from the Migrant journey: 2023 report shows that of those arriving on work routes in 2018, 38% still held valid or indefinite leave 5 years later. This suggests most workers do not remain in the UK indefinitely, although this percentage has increased, from 23% for workers arriving in 2012.

The proportion who remain and are granted indefinite leave varies by route, and is considerably higher in the ‘Investor, business development and talent’ visa category where 77% of main applicants held valid leave to remain or indefinite leave to remain after 5 years. ‘Temporary Workers’ by the nature of their visa are less likely to stay long term, needing to obtain a new type of visa to do so, and only 14% who arrived on a temporary worker visa still held leave 5 years later.

2.1 Graduate route

The Graduate route was introduced on 1 July 2021 and allows students who have successfully completed a bachelor’s degree, postgraduate degree or other eligible course to stay in the UK for a period after their studies to work or look for work.

A total of 147,051 Graduate route extensions were granted to main applicants in the year ending June 2024, 49% higher than in the year ending June 2023 (98,396). The Graduate route accounted for 39% of extensions to main applicants into the Work category.

There were also 49,721 grants to dependants on the Graduate route in the year ending June 2024, which was more than twice the number compared to the previous year (21,411).

In the year ending June 2024, Indian nationals represented the largest group of students granted leave to remain on the Graduate route (67,529), representing almost half (46%) of grants of Graduate route extensions to main applicants.

Around a third (32%) of students who came to the end of their studies in 2023 switched to the Graduate route (source: Analysis of migrants use of the Graduate route ).

3. Other related data

The Home Office publishes the number of ‘Skilled Worker’ and ‘Health and Care Worker’ visa applications by month in the Monthly monitoring of entry clearance visa applications publication.

The Office for National Statistics ( ONS ) publish long-term international migration estimates . These are statistics under development that estimate the number of people immigrating to and emigrating from the UK for 12 months or more.

According to the Labour Force Survey statistics from April to June 2024, published in the ONS Labour market overview, UK: August 2024 release , the number of non-UK nationals working in the UK has increased by 179,000 (+4%) in the past year to 4.47 million. There were an estimated 2.35 million non- EU nationals working in the UK, 214,000 higher (+10%) than a year earlier and an estimated 2.12 million EU nationals working in the UK, 35,000 lower (-2%) than a year earlier.

3.1 Sponsorship licensing for work and study

At the end of June 2024, there were 102,167 organisations and institutions registered as licensed sponsors for work and study.

Home Office management information indicates that there were 11,620 decisions on applications for sponsor licences between April to June 2024, 27% more than in April to June 2023 (9,153). Of these, 8,098 licenses were granted, and 3,522 applications were not granted (including both applications withdrawn and those rejected).

This data also shows that there were 50,488 decisions made in the year ending June 2024, compared to 32,791 in the year ending June 2023. Of these decisions, 38,235 licences were granted (51% higher than the year before), and 12,253 were not granted (including both applications withdrawn and those rejected).

4. About these statistics

The statistics in this section provide an indication of the number of people who have an intention to enter the UK for work reasons. Further information and definitions can be found in the user guide .

Entry clearance visas allow an individual to enter and stay in the UK within the period for which the visa is valid. Data in this section refers to the number of Entry clearance visas granted for work reasons within the period. If an individual was granted a visa more than once in a given period, this has been counted as multiple grants in the statistics.

Year-on-year comparisons of the number of decisions can be affected by quarterly fluctuations in the data. These fluctuations can be examined in the quarterly data in the published tables. Year ending comparisons will also include impacts resulting from the travel restrictions put in place during the COVID-19 pandemic.

Extensions of temporary stay in the UK relate to individuals inside the UK extending or changing the status of their right to stay in the UK. An individual is required to apply for an extension or change in status before their existing permission to enter or stay in the UK expires.

The statistics in this chapter show the number of grants and refusals on applications for extension of temporary stay in the UK. One individual may have made multiple applications for an extension, so may account for multiple decisions. Data in this section includes dependants and takes account of the outcomes of reconsiderations and appeals.

We have temporarily paused publication of the ‘Sponsored work visas by occupation and industry’ datasets, with the latest available data currently up to the end of March 2024. For applications in sponsored work visa routes, the Home Office has to use the latest Standard Occupational Classification (SOC) framework, which is a common classification of jobs in the UK (for example, care workers or cyber security professionals) overseen by the Office for National Statistics ( ONS ). The pause is to allow us to investigate the differences between the old and new ONS frameworks and apply the new structure to Home Office sponsorship data. The intention is for this to enable us to produce comparable figures pre and post-change, and allow for future reporting that preserves the integrity and consistency of the analysis.

5. Data tables

Data on immigration for work can be found in the following tables:

Sponsorship summary tables

Detailed sponsorship datasets

Detailed occupation datasets

Entry clearance visas summary tables

Detailed entry clearance visas datasets

Admissions summary tables

Extensions summary tables

Detailed extensions datasets

Migrant journey: 2023 report

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We’re always looking to improve the accessibility of our documents. If you find any problems or have any feedback relating to accessibility, please email us.

See section 6 of the ‘ About this release ’ section for more details.

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