When we talk false positives in psych stats, it is usually in the context of NHST, which is abstract and tricky to understand, no matter how many normal curves you draw on the dry erase board. We also tend to frame it in really statsy terms, like alpha and beta, which are also tricky and sort of abstract, no matter how many times you repeat .05 .05 .05. In all things statistics, I think that abstract concepts are best understood in the context of real-life problems. I also think that statistics instructors need to emphasize not just statistics but statistical thinking and reasoning in real life. To continue on a theme from my last post, students need to understand that the lessons in psych stats aren't just for performing statistics and getting a good grade, but also for improving general critical thinking and problem-solving in day to day life. I also think that our in-class examples can be too sterile. They may explain Type I/II error accurately, but we tend to only ask our stude...

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.

I just used this example in class, and I realized that I never shared it on my blog. I really love this example of Type II error (and some other stuff, too). So here it goes. http://www.achievement.org/autodoc/page/mar1int-1

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 ...