http://xkcd.com/1725/ This comic is another great example of allowing your student to demonstrate statistical comprehension by explaining why a comic is funny. What does the r^2 indicate? When would it be easy to guess the direction of the correlation?  More on that via this previous blog post .

I have shared Kristoffer Magnusson's fantastic visualizations of statistical concepts here previously ( correlation , Cohen's d ). Here is another one that helps to explain confidence intervals , and how the likelihood of an interval containing true mu varies based on interval size as well as the size of the underlying sample. The site is interactive in two ways. 1) The sliding bar at the top of the page allows you to adjust the size of the confidence interval, which you can read in the portion of the page labeled "CI coverage %" or directly above the CI ticker. See below. 2) You can also change the n-size for the samples the simulation is pulling. The site also reports back the number of samples that include mu and the number of samples that miss mu (wee little example for Type I/Type II error). How to use it in class: Students will see how intervals increase and decrease in size as you reset the CI percentage. As the sample size increases, the range ...

Take a look at this interactive, statistical decision making flow chart. I think that almost every statistics text includes a flow chart, but the interactive piece of this, and its ability to immediately provide the reader with information on the appropriate analysis AND software assistant is something your students can't get from paper versions of same. The flow chart is based on Andy Field's work. I discovered this tool via Reddit. I'm including that Reddit thread because the person that created the thread (commentor4) states that they also created the flow chart. So, you are lead through a series of questions (read this from the bottom up). After you provide the necessary information, the page provides you with a quick definition of the test you should conduct as well as links to instruction using popular statistical packages.

This syllabus for  PSY 607: Everything is Fucked ,  made the rounds last week. The syllabus is for a course that  purports  that science is fucked. The course readings are a list of articles and books that hit on the limitations of statistics and research psychology ( p -values, shortcomings of meta-analysis, misuse of mediation, replication crisis, etc.). PSY 607 isn't an actual class ( as author/psychologist/blogger Srivastava explains in this piece from The Chronicle ) but it does provide a fine reading list for understanding some of the current debates and changes in statistics and psychology.  Most of articles are probably too advanced for undergraduates but perfectly appropriate for teaching graduat e students about our field and staying up to date as instructors of statistics. Here is a link to the original blog post/syllabus. 

This study, which found a link between maternal Tylenol usage during pregnancy and ADHD, has been making the rounds, particularly in the Academic Mama circles I move in. Being pregnant is hard. For just about every malady, the only solution is to stay hydrated. With a compromised bladder. But at least pregnant women have Tylenol for sore hips and bad backs. For a long time, this has been the only safe OTC pain reliever available to pregnant women. But a recent research article has cast doubt on this advice. A quick read of this article makes it sound like you are cursing your child with a lifetime of ADHD if you take Tylenol. A nd this article has become click-bait fodder. But these findings have some pretty big caveats.  Harris published this reaction piece at NPR . It is a good teaching example of media hype vs. incremental scientific progress and the third (or fourth or fifth) variable problem. It also touches on absolute vs. relative risk. NOTE: There are well-documente...

This example is a little out of my wheel house, but I'm putting it up here for those of you who teach more advanced UG stats or grad stats. I have never taught Principle Component Analysis. But Anh Le, PhD candidate at Duke, provides a detailed description of PCA in R AND does so using data that your advanced undergraduate/graduate students will enjoy: Pokemon.  So, Le downloaded data for each of the 151 Pokemon (individual stats for the strengths and weakness of each Pokemon, and provided a link so that you can download the data as well). He even included the code he used to create his PCA via R AND he does a nice job talking the reader through his process and what the findings mean. At 37, I didn't realize how much my traditionally-aged college students love Pokemon. Pokemon came up in my undergraduate I/O class three years ago, and I was shocked by how much nostalgia my then-20 year old students felt for the franchise. I think that it is certainly experiencing a rev...

Unsettling. But I bet your students won't forget this example of why mean isn't always the best measure of central tendency. While the reddit user labeled this as example median's superiority, you could also use this as an example when mode is useful. As statisticians, we often fall back on to mode when we have categories and median when we have outliers, but sometimes either median or mode can be useful when decimal points don't make a lot of sense. Here is the image and commentary from reddit: And this an IG posting about the data from the same user, Mona Chalabi from fivethirtyeight. I included the Instagram because Chalabi expands a bit more upon the original data she used. https://www.instagram.com/p/BIVKJrcgW51/