I am absolutely NOT above pandering to undergraduates. For example, I use beer-related examples to illustrate t-test s,   correlation/regression , curvilinear relationships , and data mining/re-purposing . Here is some more. This data was collected to estimate how much more participants would pay for their beer if their beer was created in an environmentally sustainable manner. The answer? $1.30/six pack more. And 59% of respondents said that they would pay more for sustainable beer. NPR talked about it , as well as ways that breweries are going green. Here is a link to the original research . How to use in class: 1) The original research is shared via an open source journal . So, an opportunity to talk about open source research journals. 2) They data was collected via mTurk, another ancillary topics to discuss with your budding research methodologists. 3) The authors of the original study shared their beer survey data ! Analyze to your heart's content. 4) How c...

I'm such a sucker for beer-related statistics examples ( 1 , 2 , 3 ). Here is example 4. Now, I don't know about the rest of you psychologists who teach statistics, but I ALWAYS show the ol' Yerkes-Dodson's graph when explaining that correlation ONLY detects linear relationships but not curvilinear relationships. You know...moderate arousal leads to peak performance. See below: http://wikiofscience.wikidot.com/quasiscience:yerkes-dodson-law BUT NOW: I will be sharing research that finds claims that dementia is associated with NO drinking...and with TOO MUCH drinking...but NOT moderate drinking. So, a parabola that Pearson's correlation would not detect.  https://twitter.com/CNN/status/1024990722028650497

This is an excellent video for introducing t -tests AND finally getting the story straight regarding William Gossett, Guinness Brewery, and why Gossett published under the famous Student pseudonym. What did I learn? Apparently, Gossett DID have Guinness' blessings to publish. Also, this story demonstrates statisticians working in Quality Assurance as the original t-tests were designed to determine the consistency in the hops used in the brewing process. Those jobs are still available in industry today. Credit goes to the RStats Institute at Missouri State University.  This group has created many other tutorial videos for statistics as well.

Scott Janish loves beer, statistics, and blogging (a man after my own heart). His blog discusses home brewing as well as data related to beer. One of his statsy blog posts  looked at the relationship between average alcohol by volume for a beer style (below, on the x-axis) and the average rating (from beeradvocate.com , y-axis). He found, perhaps intuitively, a positive correlation between the average Beer Style review for a type of beer and the moderate alcohol content for that type of beer. Scott was kind enough to provide us with his data set, turning this into a most teachable moment. http://scottjanish.com/relationship-of-abv-to-beer-scores/ How to use it in class: 1) Scott provides his data. The r is .418, which isn't mighty impressive. However, you could teach your students about influential observations/outliers in regression/correlation by asking them to return to the original data, eliminate the 9 data points inconsistent with the larger pattern, and reanalyze th...

Robinson & Feltus, 2014 There appears to be a connection between political affiliation, likelihood to vote, and preferred adult beverage. If you lean right and drink Cabernet Savignon, you are more likely to vote than one who enjoys "any malt liquor" and leans left.  This Washington Post story summarizes data analysis performed by the  National Media Research Planning and Placement . NMRPP got their data from market research firm Scarborough . There is also a video embedded in the Washington Post story that summarizes the main findings. I think this is a good example of illustrating data as well as data mining pre-existing data sets for interesting trends. And beer.