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...
You know how every few months, someone Tweets about stock photos that are generated when you Google "professor"? And those photos mainly depict white dudes? See below. Say "hi" to Former President and former law school professor Obama, coming it at #10, several slots after "novelty kid professor in lab coat".
Well, Pew Research decided to quantify this perennial Tweet, and expand it far beyond academia. They used Machine Learning to search through over 10K images depicting 105 occupations and test whether or not the images showed gender bias.
How you can use this research in your RM class:
1. There are multiple ways to quantify and operationalize your variables. There are different ways to measure phenomena. If you read through the report, you will learn that Pew both a) compared actual gender ratios to the gender ratios they found in the pictures and b) counted how long it took until a search result returned the picture of a woman for a given job.
2. Replication outside of America: This research didn't just look at America but at 18 different countries.
3. Machine learning in research. For more detail on how they learned the machine to identify gender, see their methodology page.
4. Pew used data from the federal government for this project. The Bureau of Labor Statistics provided all of the actual gender break-down data for occupations.
As always, Pew provides the full report.
Well, Pew Research decided to quantify this perennial Tweet, and expand it far beyond academia. They used Machine Learning to search through over 10K images depicting 105 occupations and test whether or not the images showed gender bias.
How you can use this research in your RM class:
1. There are multiple ways to quantify and operationalize your variables. There are different ways to measure phenomena. If you read through the report, you will learn that Pew both a) compared actual gender ratios to the gender ratios they found in the pictures and b) counted how long it took until a search result returned the picture of a woman for a given job.
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| Quantifying difference by the sheer number of images. |
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| Quantifying the difference by counting how long it takes to find a picture of a woman doing the job. |
2. Replication outside of America: This research didn't just look at America but at 18 different countries.
3. Machine learning in research. For more detail on how they learned the machine to identify gender, see their methodology page.
4. Pew used data from the federal government for this project. The Bureau of Labor Statistics provided all of the actual gender break-down data for occupations.
As always, Pew provides the full report.