MOOCs aren't just for current students. I think they can serve as professional development for faculty members as well. I don't have time for a MOOC during the school year, but I am committing to doing one this summer. I think that instructors can approach MOOCs in two ways: 1) professional development, and 2) a search for improved pedagogy. As professional development, learn a new statistical skill or freshen up a dormant one. Learn R. Learn Python. Freshen up on your non-parametric skillz. Take a course on data mining or using statistics in order to gain business insights. Unofficial documentation of your course progress is typically offered just by taking the course. Official documentation/grade reports are usually available for a reasonable fee (my husband has taken a few such philosophy courses and paid around $50 for the official documentation). Another way to use these courses: Don't take them to learn new skills, take them to learn new ways to teach your old...

Hackathorn, Ashdown, & Rife just shared some great resources for using humor to teach statistics.  In their own words, " This resource consists of a 21-page word document that reviews literature on the use of humor in teaching, describes an instrument for assessing the use of classroom humor, and offers tips on using two additional resource features specific to teaching statistics: (a) 42 visual jokes and cartoons, organized by 12 statistical topics, and (b) 12 slide presentations." You guys. It is a collection of hilarious jokes and memes to use when teaching. As well as some scholarly work about using humor to teach.  Here is a link that will download the .zip file to your computer. Here is a link to STP's Office of Teaching Resources in Psychology's Teaching Resources. Scroll on down to the Statistics, Research, and Teaching header to find this resource. A few samples of the cartoons they included:

This hilarious video is making the rounds on the Interwebz. Kudos to John Oliver and his writing team for so succinctly and hilariously summarizing many different research problems...why replication is important but not rewarded, how research is presented to the public, how researchers over-reach about their own findings, etc.  I Tweeted about this, but am making it cannon by sharing as a blog post. Note: This video has some off-color humor (multiple references to bear fellatio) so it is best suited to college aged students. I will use this in my Online and Honors classes as discussion prompts. Here are some of the prompts I came up with: 1) In your own words, why aren't replications published? How do you think the scientific community could correct this problem?  2) In your own words, explain just ONE of how a RESEARCHER can manipulate their own data and/or research findings. It should be one of the methods of manipulation described in the video. Also, don't just na...

People don't always tell the whole truth on dating websites, embellishing the truth to make themselves more desirable. This example of how OK Cupid users lie about their heights is a good example for conceptually explaining null hypothesis testing, t -tests, and normal distributions. So, Cheng, article author and data enthusiast, looked through OK Cupid data. In this article, she describes a few different findings, but I'm going to focus on just one of them: She looked at users' reported heights. And she found a funny trend. Both men and women seem to report that they are taller than they actually are. How do we know this? Well, the CDC collects information on human heights so we have a pretty good idea of what average heights are for men and women in the US. And then the author compared the normal curve representing human height to the reported height data from OK Cupid Users. See below... From http://nycdatascience.com/okcupid-scraper/, by Fangzhou Cheng  ...

I hope you are all finishing your grading and enjoying the first bit of your summer vacations (those of you who teach at colleges that run on semesters, at least!). I know that the last thing you want to think about right now is professional development. Especially professional development related to the teaching of statistics. That being said, I wanted to remind everyone that the Electronic Conference on the Teaching of Statistics (eCOTS) is happening next week (May 16-20) . As a mom and generally exhausted person, I really appreciate the chance to attend a conference in my pajamas. Here is the program . It only costs $25, includes workshops and poster sessions, and recordings from the conference. So, even if you aren't mentally ready to tackle something like this right now, you could view the materials at a later date.

My son, Artie, resting in the NICU When my second son was born via emergency c-section, he spent a week in the NICU out of an abundance of caution. It wasn't fun, but Artie pulled through just fine. He is a fat, happy four-month-old now. While we were there, I found out that many of the other NICU babies there were suffering from neonatal abstinence syndrome (NAS). They were born addicted to drugs. And those poor babies howled for hours as they were being weaned off of drugs and helped by staff. NPR's All Things Considered recently did a series about national efforts to help end NAS. Two of the segments from this series are possible learning moments for statistics and RM classes. One discusses efforts to use proper research methodology to create better treatment recommendations for NAS babies . The second discusses governmental efforts to use systematic data collection to better track NAS babies and get to the root of the problem . 1. Using clinical research to bette...

Tis the season for the end of semester teaching evaluations. And Ben Schmidt has created an interactive tool that demonstrates gender differences in these evaluations.  Enter in a word, and Schmidt's tool returns to you how frequently the word is used in Rate Your Professor  evaluations, divided up by gender and academic discipline. Spoiler alert: Men get higher ratings for most positive attributes! ...while women get higher ratings for negative attributes.  Out of class, you can use this example to feel sad, especially if you are a female professor and up for tenure. In class, this leads to obvious discussions about gender and perception and interpersonal judgments. You can also use it to discuss why the x- and y-axes were chosen. You can discuss the archival data analysis used to generate these charts. You can discuss data mining. You can discuss content analysis. You can also discuss between-group differences (gender) versus within-group differences (acade...