John Oliver demonstrates representative sampling by inviting three climate change deniers to debate 97 scientists who believe that global climate change is happening . Also, Bill Nye.

A study performed by The Public Religion Research Institute  used either a) a telephone survey or b) an anonymous web survey to question people about their religious beliefs and religious service habits. The researchers found that the telephone participants reported higher rates of religious behaviors and greater theistic beliefs. The figure below,  from a New York Times summary of the study , visualizes the main findings. The NYT summary also provides figures illustrating the data broken down by religious denomination. Property of the New York Times Participants also vary in their reported religious beliefs based on how they are surveyed (below, the secular are more likely to report that they don't believe in God when completing an anonymous online survey). Property of Public Religion Research Institute  This report could be used in class to discuss psychometrics, sampling, motivation to lie on surveys, social desirability, etc. Additionally, the sour...

This tongue-in-cheek  regression analysis found a way to predict the "Hipster Music Index" of a given artist by plotting # of Facebook shares of said artist's Pitchfork magazine review on they y-axis and Pitchfork magazine review score on the x-axis. If an artist falls above the linear regression line, they aren't "hipster". If they fall below the line, they are. For example, Kanye West is a Pitchfork darling but also widely shared on FB, and, thus demonstrating too much popular appeal to be a hipster darling (as opposed to Sun Kill Moon (?), who is beloved by both Pitchfork but not overly shared on FB). As instructors, we typically talk about the regression line as an equation for prediction, but Priconomics uses the line in a slightly different way in order to make predictions. Also, if you go to the source article, there are tables displaying the difference between the predicted Y-value (FB Likes) for a given artist versus the actual Y-value, which coul...

Updated 6/25/14: Giving credit where credit is due:  http://biostatisticsryangoslingreturns.tumblr.com/ Silly, yes. But if your students can explain why they are funny, it does demonstrate statistical knowledge.

Hi! Here is my presentation from APS . I am posting it so that attendees and everyone else can have access to the links and examples I used. If you weren't there for the presentation, a warning: It is text-light, so there isn't much of a narrative to follow but there are plenty of links and ideas and some soon-to-be-published research ideas to explore. Shoot me an email (hartnett004@gannon.edu) if you have any questions. ALSO: In the talk I reference the U.S. Supreme Court case Hall v. Florida ( also did a blog entry about this case ). Update: The court decided in the favor of Hall/seemed to understand standard error/made it a bit harder to carry out the death penalty, as discussed here by Slate). Woot woot!

Slate's The Littlest Tasters More research methods than statistics, this article describes the difficulty in determining taste preferences in wee humans who don't speak well if at all. slate.com The goods for teaching: They mention the FACE scale. The research methods described go beyond marketing research and this could be useful in a Developmental class to describe approaches used in data collection for children (like asking parents to rate their children's reactions to foods). I've used this as a discussion board prompt when discussing research ethics, both for simply conducting research with children as well as the ethics of marketing (not so healthy foods) towards children. Aside: They also describe why kids like Lunchables, which has always been a mystery to me. Apparently, kids are picky about texture and flavor but they haven't developed a preference for certain foods to be hot or cold. The Huffington Post's " You'll Never Look at ...

Tyler Vigen has has created  a long list of easy-to-paste-into-a-powerpoint graphs that illustrate that correlation does not equal causation. For instance, while per capita consumption of cheese and number of people who die by become tangled in their bed sheets may have a strong relationship (r = 0.947091), no one is saying that cheese consumption leads to bed sheet-related death. Although, you could pose The Third Variable question to your students for some of these relationships). Property of Tyler Vigens, http://i.imgur.com/OfQYQW8.png Vigen has also provided a menu of frequently used variables (deaths by tripping, sunlight by state) to help you look for specific examples. This portion is interactive, as you and your students can generate your own graphs. Below, I generated a graph of marriage rates in Pennsylvania and consumption of high fructose corn syrup. Generated at http://www.tylervigen.com/