I love my fellow Reddit data geeks over at r/dataisbeautiful . Redditor faux_pseudo created a frequency chart of the deliciousness found in a box of Cracker Jacks. I think it would be funny to ask students to discuss why this graph is misleading (since the units are of different size and the pop corn is divided into three columns). You could also discuss why a relative frequency chart might provide a better description. Finally, you could also replicate this in class with Cracker Jacks (one box is an insufficient n-size, after all) or try it using individual servings of Trail Mix or Chex Mix or order to recreate this with a smaller, more manageable sample size. Also, as always, Reddit delivers in the Comments section:

Math with Bad Drawings is a very accurately entitled blog. Math teacher Ben Orlin illustrates math principles, which means that he occasionally illustrates statistical principles. He dedicated one blog posting to probability, and what probability means in different contexts. He starts out with a fairly standard and reasonable interpretation of p :  Then he has some fun. The example below illustrates the gap that can exist between reality and reporting. And then how philosophers handle probability (with high- p statements being "true"). And in honor of the current Star Wars frenzy: And finally...one of Orlin's Twitter followers, JP de Ruiter , came up with this gem about p -values:

Statistics aficionados over at FiveThirtyEight applied statistics (specifically, tools used by epidemiologists) to the Summer of 2015  outbreak of Legionnaires' Disease  in New York. This story can be specifically used in class as a way of discussing how simple bar graphs can be modified as to display important information about the spread of disease. This news story also includes a link to a learning module  from the CDC. It takes the user through the process of creating an Epi curve. Slides 1-8 describe the creation of the curve, and slides 9-14 ask questions and provide interactive feedback that reinforces the lesson about creating Epi curves. Graphs are useful for conveying data, but even one of our out staples, the bar graph, can be specialized as they share information about the way that disease spread. 1) Demonstrates statistics being used in a field that isn't explicitly statistics-y. 2) A little course online via the CDC for your students to learn to...

Alright. This teaching idea is pretty involved. It is bigger than any one instructor and requires interdepartmental effort as well as support from The Powers that Be at your university. The U.S. Holocaust Museum hosts a number of  traveling exhibits . One in particular, " Deadly Medicine: Creating the Master Race ", provides a great opportunity for the discussions of research ethics, the protection and treatment of human research subjects, and how science can be used to justify really horrible things. I am extraordinarily fortunate that Gannon University's Department of History (with assistance from our Honors program as well as College of the Humanities, Education, and Social Sciences) has worked hard to get this exhibit to our institution during the Fall 2015 semester. It is housed in our library through the end of October. How I used it in my class: My Honors Psychological Statistics class visited the exhibit prior to a discussion day about research ethics. In...

From Reddit's Instagram...the comments section demonstrates some heart-warming statistical literacy.

This headline is a good example of a) journalists misrepresenting statistics as well as b) confidence intervals/margin of error more broadly. See the headline below: In actuality, Bernie didn't exactly take the lead over Hillary Clinton. Instead, a Quinnipiac poll showed that 41% of likely Democratic primary voters in Iowa indicated that they would vote for Sanders, while 40% reported that they would vote for Clinton. If you go to the original Quinnipiac poll , you can read that the actual data has a margin of error of +/- 3.4%, which means that the candidates are running neck and neck. Which, I think, would have still been a compelling headline.  I used this as an example just last week to explain applied confidence intervals. I also used this as a round-about way of explaining how confidence intervals are now being used as an alternative/compliment to p -values.