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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...

An interactive description of scientific replication

TL;DR:

This cool, interactive website asks you to participate in a replication. It also explains how a researcher decision on how to define "randomness" may have driven the main effect of the whole study. There is also a scatter plot and a regression line, talk of probability, and replication of a cognitive example.

Long Version: 

This example is equal parts stats and RM. I imagine that it can be used in several different ways:

-Introduce the replication crisis by participating in a wee replication

-Introduce a respectful replication based on the interpretation of the outcome variable 

-Data visualization and scatterplots

-Probability

-Aging research

Okay, so this interactive story from The Pudding is a deep dive into how one researcher's decision may be responsible for the study's main effect. Gauvrit et al. (2017) argue that younger people generate more random responses to several probability tasks. From this, the authors conclude that human behavioral complexity peaks at 25.  

The Pudding authors argue that depending on how you define "randomness", the main effect goes away.

It demonstrates both a replication, a replication in which your students can participate. It also and happen. Modify the cut-off criteria for your experimental stimuli. 

Text in image: The study received coverage across dozens of outlets. The headline: A person’s ability to be random peaks around 25 years old and declines after 60. The researchers made their data and methods public so we explored the idea of making an age guessing game. This unearthed some questions for us, so the story became about the replication crisis; the ongoing concern that it is hard to reproduce many scientific studies. We think the findings of the study are at the mercy of a single decision the researchers made to filter out questionable responses. To us, this meant the participant either misunderstood the instructions, or intentionally subverted the experiment.

I think this has a place in any RM course to introduce The Replication Crisis. Before you get to the screen grab featured above, you have the option to participate in a replication of Gauvrit et al. See below for a screen grab of the instructions for one of the replication tasks: 

 


Using the blue and black toggle button, you can look at the regression line under the two conditions. The relationship goes away when the different criteria are applied to the When they exclude 
. This is a bonus lesson on interpreting scatterplots/regression, and .



Anyway. Imma using this in both our Professional Development course to introduce the replication crisis, and in my honors statistics class for a "Discussion Day" about replication in science.


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