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/ 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. ...

I think that interactive visualizations are better than lengthy, wordy text books when it comes to illustrating statistical principles. One little GIF or interactive website can do a far better job than text or words. For example: Everything that Kristoffer Magnusson has given us (effect sizes, correlations, etc.). Here is a new tool for explaining critical regions in Intro Stats. Matthew  McBee created an interactive in shinyapps that shows how critical regions change a) depending on test, b) sample size, change of the shape of the distribution. With the ol' t-test, you can show how the critical values move around with degrees of freedom What your t-test critical values looks like at df = 3... ...versus how the those critical values look at df = 80 Also, you can do the same thing but with F curves. Andplusalso: Matt has also created shiny apps to adjust p-values for multiple comparisons , AND another one for calculating p-values based on a test statistics ...

I've shared these on my Twitter feed, and in a previous blog post dedicated to stats funnies. However,  I decided it would be useful to have a dedicated, occasionally updated blog post devoted to Twitter Statistics Comedy Gold. How to use in class? If your students get the joke, they get a stats concept. *Aside: I know I could have embedded these Tweets, but I decided to make my life easier by using screenshots. How NOT to write a response option.  Real life inter-rater reliability Scale Development Alright, technically not Twitter, but I am thrilled to make an exception for this clever, clever costume: This whole thread is awesome...https://twitter.com/EmpiricalDave/status/1067941351478710272 Randomness is tricky! And not random! ...