Malaika Handa , Amber Thomas , and Jan Diehn created a beautiful, interactive website, Colorism in High Fashion . It used machine learning to investigate "colorism" at Vogue magazine. Specifically, it delves into the differences, over time, in cover model color but also how lighting and photoshopping can change the color of the same woman's skin, depending on the photo. There are soooo many ways to use this in class, ranging from machine learning, how machine learning can refine old psychology methodology, to variability and within/between-group differences. Read on: 1. I'm a social psychologist. Most of us who teach social psychology have encountered research that uses magazine cover models as a proxy for what our culture emphasizes and values ( 1 , 2 , 3 ). Here, Malaika Handa, Amber Thomas, and Jan Diehn apply this methodology to Vogue magazine covers. And they take this methodology into the age of machine learning by using k-means cluster and pixels to deter...

As I write this, I am a parent on the first day of summer break, and I have two kids who are very different from one another. So, these hilarious examples of Type I/II error from Alison Horst really speaks to me.  Not only are these illustrations beautiful and funny, but I think they really get your students to think about one HUGE underlying issue in all of inferential statistics: Every little sample that we analyze is just one of near-infinite possible samples that could have been drawn from the underlying population (or, the sampling distribution of the sample mean). Head over to her GitHub for a funny, normal curve illustration and higher resolution versions of the above pictures. She also has numerous beautiful R and ggplot illustrations . UPDATE: 11/6/19 Alison made some super cute illustrations for a topic that is simultaneously very boring but also tricky for baby statisticians: Scales of measurement.

Two scientists, Lazris and Rifkin, want to better illustrate the risks and benefits associated with preventative medicine. They do so by asking people to imagine theaters filled with 1,000 people, and describing the costs and benefits for different preventative procedures by discussing how many people in the theater will be saved or perish based on current efficacy data. One such video can be viewed here and illustrates the absolute and relative risks associated with mammography. They are attempting to demystify statistics and better explain the risks and benefits by showing an animated theater filled with 1,000 women, and showing how many women actually have their lives saved by mammograms (see screen shot below)... ...as well as the number of women who received false positives over the course of a life time... A screen shot of the video, which is trying a new way to illustrate risk. ...the video also illustrates how a "20% reduction in breast cancer deaths" ca...

Gleaned from multiple sources (FB, Pinterest, Twitter, none of these belong to me, etc.). Remember, if your students can explain why a stats funny is funny, they are demonstrating statistical knowledge. I like to ask students to explain the humor in such examples for extra credit points (see below for an example from my FA14 final exam). Using xkcd.com for bonus points/assessing if students understand that correlation =/= causation What are the numerical thresholds for probability?  How does this refer to alpha? What type of error is being described, Type I or Type II? What measure of central tendency is being described? Dilbert: http://search.dilbert.com/comic/Kill%20Anyone Sampling, CLT http://foulmouthedbaker.com/2013/10/03/graphs-belong-on-cakes/ Because control vs. sample, standard deviations, normal curves. Also,"skewed" pun. If you go to the original website , the story behind this cakes has to do w...

This news story discusses medical advice regarding dates for recommended annual mammograms for women. Of particular interest for readers of this blog: Recommendations for regular mammograms are moving later and later in life. Because of the very high false positive rate associated with mammograms and subsequent breast tissue biopsies. However, women who have a higher probability (think genetics) are still being advised to have their mammograms earlier in life. Part of the reason that these changes are being made is because previous recommendations (start mammograms at 40) were based on data that was 30-40 years old ( efficacy studies/replication are good things!). Also, I generally love counter-intuitive research findings: I think they make a strong argument for why research and data analysis are so very important. I have blogged about this topic before. This piece by Christy Ashwanden  contains some nice graphs and charts that demonstrate that enthusiastic preventative care ...

This article describes how the Pennsylvania Department of Corrections is using risk assessment data in order to predict recidivism, with the hope of using such data in order to guide parole decisions in the future. So, using data to predict the future is very statsy, demonstrates multivariate modeling, and a good example for class, full stop. However, this article also contains a cool interactive tool, entitled "Who Should Get Parole?" that you could use in class. It demonstrates how increasing/decreasing alpha and beta changes the likelihood of committing Type I and Type II errors. The tool allows users to manipulate the amount of risk they are willing to accept when making parole decisions. As you change the working definition of a "low" or "high" risk prisoner, a visualization will startup, and it shows you whether your parolees stay out of prison or come back. From a statistical perspective, users can adjust the definition of a low, medium, and h...

I take credit for none of these. A few have been posted here before. by Rayomond Biesinger, http://fifteen.ca/ Creator unknown, usually attributed to clipart? http://www.sciencemag.org/content/331/6018.cover-expansion https://www.flickr.com/photos/lendingmemo/ https://lovestats.wordpress.com/2014/11/10/why-do-kids-and-you-need-to-learn-statistics-mrx/ http://memecollection.net/dmx-statistics/ 9/23/15 Psychometrics: Interval scale with proper anchors 2/9/16 4/19/16 4/28/16 "Symbols that math urgently needs to adopt" https://mathwithbaddrawings.com/2016/04/27/symbols-that-math-urgently-needs-to-adopt/ http://www.mrlovenstein.com/ http://www.smbc-comics.com/ 9/8/16 2/9/2107 https://hbr.org/2017/02/if-you-want-to-motivate-employees-stop-trusting-your-instincts https://www.theguardian.com/politics/2017/jan/19/crisis-of-statistics-big-data-democracy?CMP=share_btn_tw 2/13/17 ...