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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...

Data can be equity: Merging of Major League Baseball and Negro League Baseball data.

I know it is January 2025, but I want to write about something that happened during the Spring of 2024.

I think it is a story about how it is never too late to do the right thing, making it great thing to think about here at the New Year. Data can't undo the past, but the way we manage it moving forward can provide the opportunity for some measure of equity.

Back in May, professional baseball decided to include Negro League (NL), which existed from 2910 to 1948, baseball stats as part of Major League Baseball (MLB) stats. This is was done to allow for proper recognition of talented ML players. This changed some storied records for the league:

https://www.mlb.com/news/stats-leaderboard-changes-negro-leagues-mlb

This was a lot more than merging a couple of spreadsheets. As such, this story also serves as a lesson in data management and making desperate datasets the same. One that is a lot more moving than your typical story of data-cleaning. The following screenshots are from:  https://www.mlb.com/news/mlb-negro-league-stats-added-after-statistical-review-committee-announces-findings

First, they had to find the data. Since the Negro League scores weren't authenticated at the time, data detectives have participated in "box-score archeology". 



Then they needed to decide how to make different leagues, with different numbers of games, the same. As such, data expressed as a proportion, not a count, are where you see NL players shine.



Thanks COVID? It appears that part of the journey of merging the data occurred during COVID, with the league coped with how to integrate statistics collected during the irregular 2020 season.







For more in-depth reading, see:

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