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/

a) The addition of this post means that I now have TWO Snoop Dogg blogg labels  for this blog. b) Daniels' graph allows students to see archival data (and research decisions used when deciding how to analyze the archival data as well as content analysis) in order to determine which rapper has the largest vocabulary. Here is Matthew Daniels interactive chart detailing the vocabularies of numerous, prominent rappers. Daniels sampled each musician's first 35,000 lyrics for the number of unique words present. He went with 35,000 in order to compare more established artists to more recent artists who have published fewer songs. (The appropriateness of this decision could be a source of debate in a research methods class.) Additionally, derivatives of the same word are counted uniquely (pimps, pimp, pimping, and pimpin count as four words). This decision was guided, from what I can gather, by the time of content analysis performed. Property of Matthew Daniels...note: The ori...

If you are going to the Association for Psychological Science annual convention in San Francisco later this month AND you are attending the Teaching Institute, I will be giving a presentation on Teaching Undergraduates to See Statistics . The talk will feature tips for engaging students via humor and current events AND share some unpublished data about using discussion boards in a statistics classes as well as an activity that introduces students to the growing trend of Big Data. Hope to see some of you there!

Here are two articles, one from The Observer and one from Perspectives on Psychological Science . The PPS article, by Chew and Dillion, is a call for more research to study statistics anxiety in the classroom. Chew and Dillon provide a thorough review of statistics anxiety research, with a focus on antecedents of anxiety as well as interventions (The Observer article is a quick summary of those interventions) and directions for further research. I think Chew and Dillion make a good case for why we should care about statistics anxiety as statistics instructors. As a psychologist who teaches statistics, I find that many of my students are not in math-related majors but can still learn to think like a statistician, in order to improve their critical thinking skills and prepare them for a data/analytic driven world after graduation. However, their free-standing anxiety related to simply being in a statistics class is a big barrier to this and I welcome their suggestions regarding the re...

This story describes a 1966 journal article that argues that signing an informed consent isn't the same as truly giving informed consent. I think this is a good example for the ethics section of a research methods class as it demonstrates some deeply unethical situations in which participants weren't able to give informed consent (prisoners, non-English speakers, etc.). Indeed, the context within which the informed consent is provided is very important. It also provides a historical context regarding the creation of Institutional Review Boards. The original 1966 article is here .

When I look at the analytic data for my blog, I notice a lot of people come here after Googling "stats memes" or "math memes" or "statistics humor". Being a data-driven sort of human, I have posted my collection of memes inspired by teaching Introduction to Statistics using SPSS. They do reflect common mistakes/stumbling blocks that I see semester after semester. I think they draw student attention towards commonly-made mistakes in a way that is not threatening. And it puts me one step closer to my ultimate goal of teaching statistics using nothing but memes and animated .GIFS  Make your own via http://memegenerator.net/ . If they are hilarious and statsy, please consider sharing them with me. UPDATE: 11/25/16

If you teach social psychology, you are probably familiar with Dr. Jon Mueller's Resources for the Teaching of Social Psychology website .  You may not be as familiar with Mueller's Correlation or Causation website, which keeps a running list of news stories that summarize research findings and either treat correlation appropriately or suggest/imply/state a causal relationship between correlational variables. The news stories run the gamut from research about human development to political psychology to research on cognitive ability. When I've used this website in the past, I have allowed my students to pick a story of interest and discuss whether or not the journalist in question implied correlation or causation. Mueller also provides several ideas (both from him and from other professors) on how to use his list of news stories in the classroom.

UPDATE! This website is not currently available.  Kevin Wu's Graph TV  uses individual episode ratings (archival data via IMDB ) of TV shows, graphs each episode over the course of a series via scatter plot, and generates a regression line. This demonstrates fun with archival data as well as regression lines and scatter plots. You could also discuss sampling, in that these ratings were provided by IMDB users and, presumably, big fans of the shows (and whether or not this constitutes representative sampling). The saddest little purple dot is the episode Black Market. Truth!

Now, I am immediately suspicious of a website entitled "MathIsFun" (I prefer the soft sell...like promising teaching aids for statistics that are, say, not awful and boring). That being said, t his app. from mathisfun.com  may be an alternative to going cross-eyed while reading z-tables in order to better understand the normal distribution. mathisfun.com With this little Flash app., you can select z-scores and immediately view the corresponding portion of the normal curve (either from z = 0 to your z, up to a selected z, or to the right of that z). Above, I've selected z = 1.96, and the outlying 2.5% of the curve is highlighted.  Now, this wouldn't work for a paper and pencil exam (so you would probably still need to teach students to read the paper table) but I think this is useful in that it allows students to IMMEDIATELY see how z-scores and portions of the of the curve co-vary.