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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...

Facebook Data Science's "What are we most thankful for?"

Recently, a Facebook craze asked users to list three things you are thankful for for five days. Data scientis ts Winter Mason, Funda Kivran-Swaine,  Moira Burke, and Lada Adamic  at Fa cebook have analyzed this dat a to better understand the patterns of gratitude publically shared by Facebook users. The data analysts broke down data by most frequently listed gratitude topic: Most frequently "liked" gratitude posts: (lots of support for our friends in recovery, which is nice to see). Gender differences in gratitude...here is data for women. The wine gratitude finding for women was not present in the data for men. Ha. Idiosyncratic data by state. I would say that Pennsylvania's fondness for country music rings true for me. How to use in class: This example provides several interesting, easy to read graphs, and the graphs show how researchers can break down a single data set in a variety of interesting ways (by gender, by age, by state). Add...

Diane Fine Maron's "Tweets identify food poisoning outbreaks"

This Scientific American podcast by Diane Fine Maron describes how the Chicago Department of Public Health (CDPH) used Twitter data to shut down restaurants with health code violations. Essentially, the CDPH monitored Tweets in Chicago, searching for the words "food poisoning". When such a tweet was identified, an official at CDPH messaged the Twitterer in question with a link to an official complain form website. The results of this program? "During a 10-month stretch last year, staff members at the health agency responded to 270 tweets about “food poisoning.” Based on those tweets, 193 complaints were filed and 133 restaurants in the city were inspected. Twenty-one were closed down and another 33 were forced to fix health violations. That’s according to a study in the journal  Morbidity and Mortality Weekly Report.  [Jenine K. Harris et al,  Health Department Use of Social Media to Identify Foodborne Illness — Chicago, Illinois, 2013–2014 ]" I think this is ...

Free stats/methods textbooks via OpenStax

  OpenStax  CNX  " is a dynamic non-profit digital ecosystem serving millions of users per month in the delivery of educational content to improve learning outcomes. " So, free text books that can be easily downloaded. Including nearly 7,000 free statistics text books as well as over 1,500  research  methods texts . How OpenStax works (viahttp://cnx.org/about) I like this format because it is free but also because it is flexible enough that you can pick and choose chapters from different text books to use in a class. Additionally, if you are feeling generous, you can upload your own content to share.