This is a fine example of using misleading statistics to try and make an argument. USA Today tweeted out this graphic , related to some data that was collected by some firm. There appear to be a number of method issues with this data, so a number of ways to use this in your class: 1) False Dichotomy:  Survey response options should be mutually exclusive. I think there are two types of muddled dichotomies with this data: a) What is "essential"? When my kids were younger, I had an online subscription for diapers. Those were absolutely essential and I received a discount on my order since it was a subscription. However, according to this survey dichotomy, are they an indulgence since they were a subscription that originated online. b) Many purchases fall into multiple categories. Did the survey creators "double-dip" as to pad each mean and push the data towards it's $18K conclusion? Were participants clear that "drinks out with frien...

You know how every few months, someone Tweets about stock photos that are generated when you Google "professor"? And those photos mainly depict white dudes? See below. Say "hi" to Former President and former law school professor Obama, coming it at #10, several slots after "novelty kid professor in lab coat". Well, Pew Research decided to quantify this perennial Tweet, and expand it far beyond academia. They used Machine Learning to search through over 10K images depicting 105 occupations and test whether or not the images showed gender bias.  How you can use this research in your RM class: 1. There are multiple ways to quantify and operationalize your variables . There are different ways to measure phenomena. If you read through the report, you will learn that Pew both a) compared actual gender ratios to the gender ratios they found in the pictures and b) counted how long it took until a search result returned the picture of a woman for a given j...

Question-wording matters, friends! This example shows how question order and question-wording can affect participant response. This is a good example for all of your research methods and psychometrics students to chew on. Pew Research asked people if they believed in evolution . They did so in three different ways, which lead to three different response patterns. 1) Prior to asking about evolution, the asked whether or not the participant believes in God. 2) Asked participants if they believed in evolution. If they said "yes", they asked the participant whether or not they believe that a higher power guides evolution. 3) They asked participants if they believed in evolution and gave participants three response options:     a) Don't believe in evolution.     b) Believe in evolution due to natural selection.     c) Believe in evolution guided by a higher power. Responses to Option 1: Responses to Options 2. and 3. Oh, the classroom discus...

Legit funny, but also a gentle way to remind our students that Word will not flag a correctly spelled word that is not the word you want.

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.

1) I disagree with Marc Stein's title  for this article. I don't think NBA great Steven Curry's devotion to his favorite snack is a problem. I think it is a very, very endearing example of someone who knows themselves, knows what works for them, and embraces it. A quote from the article describing Curry's popcorn devotion: 2) Curry loves popcorn so much that at the behest of the New York Times, Curry rated popcorn served at all of the pro-basketball arenas: Here is an example of the assessment form:  And here are the results of the NYT's n=1 study. In addition to a statistics class example, I think this could also be used in an I/O class to explain Subject Matter Experts ;)

While I was attending the Teaching Institute at APS, I attended Wind Goodfriend's talk about using case studies in the classroom. Which got me thinking about fun case studies for statistics. But not, like, the classic story about Guinness Brewery and the t-test . I want case studies that feature a regular person in a regular job who used their personal expertise to deduce from data to do something great. An example popped into my head while I was walking my dog and listening to the Hamilton soundtrack: Hercules Mulligan. Okieriete Onaodowan, portraying Hercules Mulligan in Hamilton He was a spy for America during the American Revolution. He was a tailor and did a lot of work for British military officers. This gave him access to data that he shared through a spy network to infer the timing of British military operations. Here is a better summary, from the CIA:  I like this example because he wasn't George Washington. And he wasn't Alexander Hamilton. He had t...

NOTE: SPOILERS ABOUND. The Good Place is on NBC. And I love it.  At the heart of the show is one demon's (Michael) efforts to create a new version of hell that is only hellish because every person is already being punished by who and what they are. Right, I know. Anyway, in Season 3, Episode 11, Michael's bosses argue that this hell isn't working because it actually leads to self-improvement and fulfillment for everyone is supposed to be tormented. And the bosses argue that the self-improvement is a fluke. So one of the other characters, a philosopher named Chidi, suggests...SCIENTIFIC REPLICATION!! The whole episode is great, but here are some screenshots to get started.

I am a fan of explaining the heart of a statistical analysis conceptually with words and examples, not with math. Information Is Beautiful has a gorgeous new interactive, Diversity in Tech , that uses data visualization to present gender and ethnic representation among employees at various big-name internet firms. I think this example explains why we might use Chi-Square Goodness of Fit. I think it could also be used in an I-O class. So, what this interactive gives you is a list of the main, big online firms. And then the proportions of different sort of people who fall into each category. See below: When I look at that US Population baseline information, I see a bunch of expected data. And then when I see the data for different firms, I see Observed data. So, I see a bunch of conceptual examples for chi-square Goodness of Fit. For example, look at gender. 51% of the population is female. That is you Expected data. Compare that to data for Indiegogo. They have 50% female e...

In my stats classes, we discuss popular claims that have been proven/disproven by research. So, learning styles. Vitamins. One claim we dig into are the wide array of claims made about the health benefits of different foods and folk beliefs about nutrition. But how to get into it? That is such a big field, looking at different foods used for different conditions. Send your students to InformationIsBeautiful's Snake Oil Super foods , which sorted through all of good studies and created an interactive data viz to summarize. For instance, these are three foods, backed by science, for very specific issues: BUT GET THIS: If you scroll over any of them, you get a quick summary of the findings AND a link to the research article. See below for Oats. NOICE. The information isn't limited to slam dunks, either, it fleshes out promising foods and weak links as well. AND...this is great...below the visualization there is all sorts of information on their methodo...

TL; DR- Effect size interpretation requires more than numeric interpretation of the effect size. You need to think about what would be considered a big deal, real-life change worth pursuing, given the real-world implications for your data. For example, there is a  malaria vaccine with a 30% success rate undergoing  a large scale trial in Malawi . If you consider that many other vaccines have much higher success rates, 30% seems like a relatively small "real world" impact, right? However, two million people are diagnosed with malaria every year. If science could help 30% of two million, the relatively small effect of 30% is a big deal. Hell, a 10% reduction would be wonderful. So, a small practical effect, like "just" 30%, is actually a big deal, given the issue's scale. How to use this news story: a) Interpreting effect sizes beyond Cohen's numeric recommendations. b) A primer on large-scale medical trials and their ridiculously large n-sizes and tra...

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 ...

There are a lot of lessons in this one study  (Craig, Nelson, & Dixson, 2019): Within subject design, factorial ANOVA and interactions,and data is available via OSF. Let's begin: TL: DR: The original study looked and the presence or absence of beards and whether or not this affected participants' ability to decode the emotional expression on a man's face. Or, more eloquently: TL: DR: Their stimuli were pictures of the same dudes with and without beards. And those weren't just any dudes, they had been trained in the Ekman facial coding system as to make distinct expressions. Or... One participant, rating the same man in Bearded vs. Non-bearded condition, provides a clear example of within subject research design. This article also provides examples of interactions and two-way ANOVA. Here look at aggression ratings for expressing (happy v. angry) and face hairiness (clean-shaven v. beard). Look at that bearded face interaction! Bearded guy...