This is just a little one-way ANOVA with three levels. You can use it in class to assess, review, or teach the topic. It comes from the following article by Rivera-Chavez et al . https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2843427 TL:DR- They gathered data and performed a one-way ANOVA that suggests that people with emerging psychosis have glutamate (a neurotransmitter) levels that are higher than both controls and folks who have schizophrenia diagnoses. Even if you aren't an expert on this topic, JAMA's ready to explain the relevance of this study to your students: Reasons why I love this as an example for my novice psychological statisticians: 1. This data is related to psychology, a simple one-way ANOVA with three levels, and was recently published, making it a nice little refresh to my course content. There are other analyses in the article, but here are the ANOVA results. 2. I emphasize that my students learn how to read and write statistical findings, so h...

Hey, I have kids, so I knew that Mr. Beast made a video where 100 competitors, one person from every age from 1-100, competed in feats for $250K.  In the very first competition, competitors ran a footrace, and the top five in each age category advanced to the next round.  Image from: https://www.reddit.com/r/data_irl/comments/1r15ecq/data_irl/ Anyway, in doing so, Mr. Beast inadvertently created a jitter plot using humans. Age group/starting line is at the top of the image, with the checkered finish line at the bottom. The dark blue/light blue columns are a nice touch, too. How to use in class: 1) Pander to your students by using a Mr. Beast example. 2) Ask your students to interpret the data. What can be learned from this image? The basics of bee plots. As expected, the 11-20, 21-30, and 31-40 groups ran the fastest. However, I think 31-40 was the slowest of the three groups, with a bit more variability.  3) I guess this would also be a good example of a non-linear ...

This example illustrates how better assessment has likely increased in autism diagnoses (as opposed to the increase being due to vaccines or hysterical parents). It does a good job of illustrating truncated y-axes and the surveillance effect. It also reminds our psychology majors that we have many professional allies and colleagues outside of psychology. Like speech language pathologists.  I found these examples (see below) on Facebook from speech-language pathologist/excellent science communicator  Mrs. Speechie P.   AKA Andi Putt. How to use in class: 1. Truncated y-axis I like how she mentions that truncated y axes can be a scare tactic. I also like that she shows there are still relatively few in the total population. https://www.facebook.com/photo/?fbid=1327073175898079&set=a.463959318876140 On this theme, she shared a second image that does a really good job of showing how proper diagnosis isn't the same thing as fake/inflated diagnoses (a common argument in ant...

 There are so many ways to use this tool: Nathan Yau's Flowing Data is one of those websites I check every few days for statistical inspiration. He shares  the work of others and his own, including this  interactive bee swarm plot that illustrates salaries  for various  jobs. The bee plot, with the cursor of Psychology Teachers. https://flowingdata.com/2025/09/09/salary-and-occupation-2024/ There are many ways to use this in stats class: 1. At some point, you should talk about career exploration with your students.   2. Statistics students should be learning about modern data visualizations like this jitter plot, aka bee swarm plot.  3. If you cursor over any dot, you can see the 25th and 75th percentile scores and n size for that occupation's salary. 4. The size of each circle corresponds to the n size. Which I love because jitter plots do a great job of illustrating variability in a data set. However, each data point here represents an average...

 I'm surprised I haven't shared this in this space already. It is one of my favorite data points ever. Clearly, I have favorite data points. https://www.economist.com/graphic-detail/2019/07/04/are-extraterrestrials-extra-patriotic How to use in class? 1) There is data for EVERYTHING if you look hard enough, 2) WHY might this relationship exist (heat stroke, staring at the sky, drinking, freedom, fireworks)?  If you like this example, check out my W.W. Norton & Co. textbook,  Psychological Statistics for Everyone . 

A clever YouTuber, PWA , built a channel with nearly a million followers based on animated videos that compare nations based on data. Every nation is a sassy sphere. Each grows and shrinks in size, in comparison with other nations, as the data is presented. Like this image, illustrating national debt as a portion of GDP... I swear, it is funny and engaging without trying too hard. Also, for better or worse, framing data sharing and visualization as a thing that can make you a successful influencer WILL grab your students' attention. I think these videos would make good  bell-ringer s ( TM Janet Peters) for the start of your class. This influencer makes a ton of videos, and they aren't all related to data, FYI. Here are a few good examples for Stats class:  National Debt: In this clip, the countries compare their national debt. This video discusses some of the choices statisticians make with their data. For example, they compare their national debt in USD and then compare...

Look at how Dima Yarovinsky turned the Terms and Conditions documents for several social media platforms into foreboding and beautiful art/bar graphs illustrating how much we sign away without reading. Note: He even uses the X axis to describe the length of and reading time for each T&C statement!  I think data is beautiful. This example does a good job of showing the beauty and impact of good data visualizations to my students. This isn't a huge example to use in class, but I will use it next time I discuss bar graphs. For more from the artist, in his own words, visit his webpage .  For a thought review of this art, see this article by Emma Taggart .

I have found plenty of class inspiration on Reddit. Various subs have provided a  new way to explain mode   and median  and great, intuitive data to teach  correlation . However, much as a reverse-coded item on a scale can be used to get to the opposite of what you are asking about, r/DataIsUgly is rife with examples of how NOT to do data as to teach how to create good data visualizations. Very recently, I shared this example from r/DataIsUgly to illustrate why NOT to truncate the Y axis .  And...this sub is filled with people like us. People who love to proofread and notice data crimes. For example: How to use it in class? Can your students figure out why these data visualizations are...less than optimal? Can they fix them? They could be a fun prompt for extra credit points or a discussion board.

Why did I find this after my textbook was published? Damn it. I have a whole section about how Y-axis manipulation can make small differences look huge and then...I find this. Damn it. Source:   https://www.reddit.com/r/dataisugly/comments/1hjr01o/height_of_female_popstars/

My students struggle with sleeping and are distracted by electronics. This interactive activity allows them to think about their sleep relative to norms regarding age and sex. It also dives deeply into how sleep changes over a person's lifespan, which is a topic suitable for non-static classes like Health or Developmental.   https://www.washingtonpost.com/wellness/interactive/2024/sleep-data-survey-americans/ *You need a WaPo subscription or paywall buster to get to this interactive. Like this one! https://www.removepaywall.com/search?url=https://www.washingtonpost.com/wellness/interactive/2024/sleep-data-survey-americans/ Here is a quick interactive that a) lets your students see how well they sleep, in comparison to their demographic and b) think about median data and percentile data.  1. Repursped, gently used data is really everywhere. This interactive uses data from the Census Bureau. Which is a way to measure sleep, but not the only way. 2. Median and percentil...

Many thanks to Dr. Sara Appleby for sharing this data with me!! I really enjoy silly data, like this  one from Jayson Jenks, writing for  The Athletic,  which shows how many Uncrustables each team eats per  week. Well, data from the teams that elected to participate and/or didn't make their own PB and Js. The whole article is fun, so give it a read. It makes sense that hungry athletes would go for a quick, calorie-dense, nostalgic snack containing protein.  Here is the data visualization:  Damn, Denver.  I entered this data into a spreadsheet for all of us. Spoiler alert: The number of Uncrustables eaten per week does not vary by league (independent t -test example), and the number of wins in 2023 does not correlate with the number of Uncrustables eaten per week in 2023 (correlation/regression example). Also, for my own curiosity, I re-ran the data after deleting Denver, and it wasn't enough of a difference to achieve significance.  

 Unlike the Chartr newsletter, I love this as it feeds my fascination with data and provides interesting examples for the class. As I sit here writing (5/11/24), I am enjoying my other favorite stats newsletter, How to Read This Chart . The current newsletter discusses data visualizations used on the front page of the Post. Such as: Philip Bump lovingly curates this newsletter. One time, he found historic, unlabeled charts and asked readers for help interpreting them . I also thought this one, which compared the margin of error and sample sizes used by major national polling firms, fascinating .

Chartr|Data Storytelling   Just subscribe. It is entertaining. I mean, look at this: Like, there is a part of my brain that can just doom scroll stats content. Stats scroll? That sounds like an R function. Anyway, that part of my brain loves Chartr

  The Science of Scare project, conducted by MoneySuperMarket.com, recorded heart rates in participants watching fifty horror movies to determine the scariest of scary movies. Below is a screenshot of the original variables and data for 12 of the 50 movies provided by MoneySuperMarket.com : https://www.moneysupermarket.com/broadband/features/science-of-scare/ Here is my version of the data in Excel format . It includes the original data plus four additional columns (so you can run more analyses on the data): -Year of Release -Rotten Tomato rating -Does this movie have a sequel (yes or no)? -Is this movie a sequel (yes or no)? Here are some ways you could use this in class: 1. Correlation : Rotten Tomato rating does not correlate with the overall scare score ( r = 0.13, p = 0.36).   2. Within-subject research design : Baseline, average, and maximum heart rates are reported for each film.   3. T-tests : The is a sequel/has a sequel data can be used to perform a ...

I am in a hotel lobby in Portland, OR. I am attended Society for the Teaching of Psychology's Annual Conference on Teaching. I did a talk with my friend Jenny Kunz on syllabus redesign. We found that incorporating graphic design principles in syllabi improve retention of syllabus information.   Anyway, that reminded me of the recent passing of Burkey Belser. Who is that? He is the graphic designer who created the the labels on each and every food item sold in America. I learned about his passing from this remembrance in NPR. IT IS A FREQUENCY TABLE, Y'ALL. I never thought about it this way until, like, a week ago. After seeing these and using these for years and years. Okay, first, let's just take a moment to admire one of Belser's professional head shots. RIGHT?! Anyway, I had never heard of  Belser until I came across this remembrance on NPR: How to use in class: 1. Frequency table example. 2.Sometimes, I like to remind my students that the examples I have for them in...

This example is from The Economist and behind a paywall. However, it is worth using one of your free monthly views to see these visualizations of how much improvement Ps experience. That said, whenever I talk about antidepressants in class, I remind my students MANY TIMES that I'm not that kind of psychologist, and even if I was, I'm not their psychologist. Instead, they should direct any and all medication questions to their own psychologist. This blog post was inspired by " Antidepressants are over-prescribed, but genuinely help some patients " from The Economist, which was in turn inspired by  " Response to acute monotherapy for major depressive disorder in randomized, placebo-controlled trials submitted to the US FDA: individual participant data analysis", by M.B. Stone et al., BMJ, 2022; "Selective publication of antidepressant trials and its influence on apparent efficacy: updated comparisons and meta-analyses of newer versus older trial s", ...

Sometimes, you just need a new, simple example for a homework question or a class warm-up.   I eyeballed and entered the   data here  ( r   = -.55). Enjoy. I use this little example to explain to use the regression formula to make a prediction. Here are my slides .

 My favorite examples of bad data visualizations are the ones that use accurate data that was actually collected through seemingly ethical means but totally malign the data. The numbers are correct, the data viz is...not very truthy ( I'm looking at you, Florida. ) Especially when you mess up the data viz in a way that appears to be deliberate AND doesn't really strengthen your point. I'm also looking at you, The White House. Here is a story of a deliberate but pointless massaging of a y-axis. A story in Three Tweets. 1. The Biden Administration is doing a good job of encouraging economic growth, right? Take a gander at this bar graph. 2021 was a success...just look at the chart.  2. BUT WAIT. What's this? That y-axis is shady. I...just can't think of any software/glitch that could make this mistake by accident. ALSO: If you like Twitter, follow Graph Crimes.  3. The White House issues a correction featuring a pretty good data put, I would say.  FIN

Josh Katz and Kevin Quealy p ut together a cool interactive website to help users better understand their NYC commute . With the creation of this website, they also are helping statistics instructors illustrate a number of basic statistics lessons. To use the website, select two stations... The website returns a bee swarm plot, where each dot represents one day's commuting time over a 16-month sample.   So, handy for NYC commuters, but also statistics instructors. How to use in class: 1. Conceptual demonstration of the sampling distribution of the sample mean . To be clear, each dot doesn't represent the mean of a sample. However, I think this still does a good job of showing how much variability exists for commute time on a given day. The commute can vary wildly depending on the day when the sample was collected, but every data point is accurate.  2. Variability . Here, students can see the variability in commuting time. I think this example is e...

C ollin gleaned some archival data about ER visits in America from US Consumer Product Safety Commission. For each ER visit, there is a brief description of the reason for the visit. Collin queried punching related injuries. See his Method section below, which describes how he set the parameters for his operationalized variable. With a bit of explaining, you could also describe how Collin took qualitative data (the written description of the injury) and converted it into quantitative data: http://qz.com/582720/americas-most-prolific-wall-punchers-charted/ Then he made some charts. The age of wall punchers is right-skewed. And probably could be used in a Developmental Psychology class to illustrate poor judgment in adolescents as well as the emergence of the prefrontal cortex/executive thinking skills in one's early 20s. http://qz.com/582720/americas-most-prolific-wall-punchers-charted/ The author looked at wall punching by month of the year and uncovered a fairly un...