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Updated example of using baby names to illustrate bimodal distributions.

The thing about my way of teaching stats is that my cool, novel examples get old really fast. Like, pop culture is on a thirty-day cycle. Anyway, YEARS ago, 2014 to be exact, I blogged about illustrating bimodal data distributions  using baby name data from the Social Security Administration. Specifically, when an older name comes back into vogue, it has a bimodal distribution when you plot time on the X axis.  I stand by this example. However, all of the data illustrations come from 2014. When our traditionally aged college students were 6 or 7. Which makes this example feel a little stale. However, here is some data,  courtesy of Redditor  fevaah ,  showing  old names that are back in vogue through 2025 ! And Eleanor, Violet, Nora, and Hazel are here, illustrating bimodal distributions for a new generation. If you used my old baby-name data, this is a quick, easy refresh for your lectures (or a fun new addition if you didn't).  If this idea was help...

John Bohannon's "I fooled millions into thinking chocolate helps weight loss. Here's how."

http://io9.com/i-fooled-millions-into-thinking-chocolate-helps-weight-1707251800 This story demonstrates how easy it is to do crap science, get it published in a pay-to-play journal, and market your research (to a global audience). Within this story, there are some good examples of Type I error, p -hacking, sensationalist science reporting, and, frankly, our obsession with weight and fitness and easy fixes—also, chocolate. Here is the original story, as told to io9.com by the perpetrator of this very conscientious fraud, John Bohannon . Bohannon ran this con to expose just how open to corruption and manipulation the whole research publication process can be ( BioMed Central scandal , for another example), especially when it just the kind of research that is bound to get a lot of media attention ( LaCour scandal , for another example). Bohannon set out to "demonstrate" that dark chocolate can contribute to weight loss. He ran an actual study ( n = 26). He went on a ...

/rustid's "What type of Reese's has the most peanut butter?"

Rustid, a Reddit redditor, performed a research study in order to determine the proportions of peanut butter contained in different types of Reese's Peanut Butter candies. For your perusal, here is the original  reddit thread  (careful about sharing this with students, there is a lot of talk about how the scales Rustid used are popular with drug dealers), photo documentation via  Imgur , and a  Buzzfeed article  about the experiment. Rustid documented the process by which he carefully extracted and measured the peanut butter content of nine different varieties of Reese's peanut butter and chocolate candies. See below for a illustration of how he extracted the peanut butter with an Exact-o knife and used electronic scales for measurements. http://imgur.com/a/wN6PH#SUhYBPx Below is a graph of the various proportions of peanut butter contained within each version of the Reese's Peanut Butter Cup. http://imgur.com/a/wN6PH#SUhYBPx This example...

Tessa Arias' "The Ultimate Guide to Chocolate Chip Cookies"

I think this very important cookie research is appropriate for the Christmas cookie baking season. I also believe that it provides a good example of the scientific method. Arias started out with a baseline cookie recipe (baseline Nestle Toll House Cookie Recipe, which also served as her control group) and modified the recipe in a number of different ways (IVs) in order to study several dependent variables (texture, color, density, etc.). The picture below illustrates the various outcomes per different recipe modifications. For science! http://www.handletheheat.com/the-ultimate-guide-to-chocolate-chip-cookies Also, being true scientist, her original study lead to several follow up studies investigating the effect of different kinds of pans and flours  upon cookie outcomes. http://www.handletheheat.com/the-ultimate-guide-to-chocolate-chip-cookies-part-2 I used this example to introduce hypothesis testing to my students. I had them identify the null and alternative ...

Franz H. Messerli's "Chocolate consumption, cognitive function, and Nobel Laureates"

A chocolate study seems very appropriate for the day after Easter. Messerli's study found a strong and positive correlation between a nation's per capita chocolate consumption and the number of Nobel prizes won by that nation (see graph below). The research article is a pretty straight forward: The only statistical analysis conducted was a correlation, the journal article is very short, and it used archival data. As such, you can use this example to illustrate correlation and archival data as well as the dread "third variable" problem (by asking students to generate variables that may increase chocolate consumption as well as top-notch research/writing/peace/etc.). Property of Messerli/New England Journal of Medicine