I'm such a sucker for beer-related statistics examples ( 1 , 2 , 3 ). Here is example 4. Now, I don't know about the rest of you psychologists who teach statistics, but I ALWAYS show the ol' Yerkes-Dodson's graph when explaining that correlation ONLY detects linear relationships but not curvilinear relationships. You know...moderate arousal leads to peak performance. See below: http://wikiofscience.wikidot.com/quasiscience:yerkes-dodson-law BUT NOW: I will be sharing research that finds claims that dementia is associated with NO drinking...and with TOO MUCH drinking...but NOT moderate drinking. So, a parabola that Pearson's correlation would not detect.  https://twitter.com/CNN/status/1024990722028650497

The Ingrham, writing for The Washington Post, used data to investigate the claim that undocumented  immigrants are a large source of crime.  You may hit a paywall when you try to access this piece, FYI. Ingraham provides two pieces of evidence that suggest that undocumented immigrants are NOT a large source of crime. He draws on a  policy brief from the Cato Institute and a research study by Light and Miller  for his arguments. The Cato Institute policy brief   about illegal immigration and crime is actually part of a much larger study . It provides a nice conceptual example of a 3 (citizenship status: Native born, Undocumented Immigrant, Legal Immigrant) x 3 (Crime Type: All crimes, homicide, larceny) ANOVA. I also like that this data shows criminal conviction rates per 100K people, thus eliminating any base rate issues when comparing groups. From: https://www.washingtonpost.com/amphtml/news/wonk/wp/2018/06/19/two-charts-demolish-the-notion-that-i...

Positive, interactive linear relationships, y'all. Chase, of Overflow Data , created a scatter plot that finds that as income goes up, so does rent. Pretty intuitive, right? I think intuitive examples are good for students. Cursor over the dots to see what metro area each dot represents, or use the search function to find your locale and personalize the lesson a wee bit for your students.

This website is associated with Agresti, Franklin, and Klinenberg's text Statistics, The Art and Science of Learning from Data ( @artofstat ), and there are dozens of great interactives to share with your statistics students. Similar and useful interactives exist elsewhere, but it is nice to have such a thorough, one-stop-shop of great visuals. Below, I have included screengrabs of two of their interactive tools. They also explain chi-square distributions, central limit theorem, exploratory data analysis, multivariate relationships, etc. This interactive about linear regression let's you put in your own dots in the scatter plot, and returns descriptive data and the regression line, https://istats.shinyapps.io/ExploreLinReg/.  Show the difference between two populations (of your own creation), https://istats.shinyapps.io/2sample_mean/

Students love personalized, interactive stuff.  This website from Chirs Wilson over at Time allows your American students to enter their name and they recieve their British statistical doppleganger name in return. Or vice versa. And by statistical doppleganger, I mean that the author sorted through name popularity databases in the UK and America. He then used a Least Squared Error model in order to find strong linear relationships for popularity over time between names. How to use in class: Linear relationship LSE Trends over time

Sometimes, we need to convince our students that taking a statistics class changes the way they think for the better. This example demonstrates that one seemingly simple skill, interpreting a scatter plot, is tougher than it seems. Pew Research conducted a survey on scientific thinking in America ( here is a link to that survey ) and they found that only 63% of American adults can correctly interpret the linear relationship illustrated in the scatter plot below. And that 63% came out a survey with multiple-choice responses! How to use in class: -Show your students that a major data collection/survey firm decided that interpreting statistics was an appropriate question on their ten-item quiz of scientific literacy. -Show your students that many randomly selected Americans can't interpret a scatter plot correctly. And for us instructors: -Maybe a seemingly simple task like the one in this survey isn't as intuitive as we think it is!

http://xkcd.com/1725/ This comic is another great example of allowing your student to demonstrate statistical comprehension by explaining why a comic is funny. What does the r^2 indicate? When would it be easy to guess the direction of the correlation?  More on that via this previous blog post .

If you are familiar with financial and racial disparities that exist in the US, you can probably guess where this article is going based on its title. Kids in wealthy school districts do better in school than poor kids. Within each school district, white kids do better than African American and Latino kids. How did they get to this conclusion? For every school district in the US, the researchers used the Stanford Educational Data Archive to figure out 1) the median household income within each school district and 2) the grade level at which the students in each school district perform (based on federal test performance). This piece also provides multiple examples for use within the statistics classroom. Highly sensitive examples, but good examples none the less. -Most obviously, this data provides an easy-to-follow example of linear relationships and correlations. The SES:school performance relationship is fairly intuitive and easy to follow (see below) From the New Yor...

Data has been used to learn a bit more about the age old observation that books are always better than the movies they inspire. Fivethirtyeight writer Walk Hickey gets down to the brass tacks of this relationship by exploring linear relationships between book ratings and movie ratings.  The biggest discrepancies between movie and book ratings were for "meh" books made into beloved movies (see "Apocalypse Now"). How to use in class: -Hickey goes into detail about his methodology and use of archival data. The movie ratings came from Metacritic, the book ratings came for Goodreads. -He cites previous research that cautions against putting too much weight into Metacritic and Good reads. Have your students discuss the fact that Metacritic data is coming from professional movie reviewers and Goodreads ratings can be created by anyone. How might this effect ratings? -He transforms his data into z-scores. -The films that have the biggest movie:book rati...

Jethro Waters, Dan Peterson, Ph.D., Laurie McCollough, and Luke Norton made a pair of animated videos ( 1 , 2 ) that explain why correlation does not equal causation and how we can perform lab research in order to determine if causal relationships exist. I like them a bunch. Specific points worth liking: -Illustrations of scatter plots for significant and non-significant relationships. Data does not support the old wive's tale that everyone goes a little crazy during full moons. -Explains the Third Variable problem. Simple, pretty illustration of the perennial correlation example of ice cream sales (X):death by drowning (Y) relationship, and the third variable, hot weather (Z) that drives the relationship. -In addition to discussing correlation =/= causation, the video makes suggestions for studying a correlational relationship via more rigorous research methods (here violent video games:violent behavior). Video games (X) influence aggression (Y) via the moderato...

This is a site I found this summer during the hype surrounding the London Summer Olympics. If you enter your weight and height into the site, it will match you with the Olympian who has the most similar weight and height as to predict your ideal Olympic sport. Needless to say, there are more than two factors that determine one's ideal sport. Which is a great starting point when discussing multiple regression and making predictions. Students can discuss whether or not they've ever played the sport predicted (what is handball? I dunno) as well as list other factors that determine athletic preferences (SES, individualistic vs. collectivist tendencies, body composition, hand eye coordination, etc.).