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...

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 ;)