TL;DR: This cool, interactive website asks you to participate in a replication. It also explains how a researcher decision on how to define "randomness" may have driven the main effect of the whole study. There is also a scatter plot and a regression line, talk of probability, and replication of a cognitive example. Long Version:  This example is equal parts stats and RM. I imagine that it can be used in several different ways: -Introduce the replication crisis by participating in a wee replication -Introduce a respectful replication based on the interpretation of the outcome variable  -Data visualization and scatterplots -Probability -Aging research Okay, so this interactive story from The Pudding is a deep dive into how one researcher's decision may be responsible for the study's main effect.  Gauvrit et al. (2017 ) argue that younger people generate more random responses to several probability tasks. From this, the authors conclude that human behavioral complexity...

Sometimes, I post whole class lessons. Sometimes, I post short little example nuggets. Today I share the latter.  This one is a brief, easy-to-understand example of why AI only learns what we teach it and how even a smarty pants computer can get a little confused about correlations and what they mean. A great way to introduce ML, AI, problems with both, and even discuss correlation and predictions and regression. https://twitter.com/hoalycu/status/1507770891786096643...in my head, I imagine that AI was just judging comic-sans font. The text in this tweet was from a MIT Technology Review article by WIll Douglas Heaven: https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/ If you want to go deeper with this example, I strongly recommend reading Dr. Cat Hicks's thread about this post:  https://twitter.com/grimalkina/status/1508095358693302275 .

A bit over a year ago, Susan Nolan asked me to chair the Statistical Literacy, Reasoning, and Thinking, Guidelines 2.0 for the Society for the Teaching of Psychology.  We were asked to explore and provide guidance for a) teaching statistical thinking in intro psychology and b) understanding how statistical thinking is taught across the psychology curriculum. This post will highlight the accomplishments of the first group, which created easy-to-implement teaching exercises that emphasize statistical reasoning skills in Intro Psych. The Guidelines 1.0 group provided lists of topics included in Intro Psych. The Guidelines 2.0 convened and created a series of brief, easy-to-apply exercises that correspond to the core topics typically taught in Intro. The sub-committee chair, Dr. Garth Neufeld, shared his considerable expertise about Intro Psychology to lead the group and center each exercise in American Psychological Association and American Statistical Association guidelines for under...