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Showing posts with the label goodness of fit

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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 ode to Western Pennsylvania, in chi-square form

I've been writing this blog, statistics pedagogy articles, chapters, and a whole statistics textbook for over ten years. I'm at the point where I see silly stuff on the internet, and it automatically translates to a statistics example. Like this recent Tweet from Sheetz about the Pirates/Philly series this weekend. https://x.com/sheetz/status/1923397811778785489 This is an unapologetically Western PA tweet. I will be using it as a chi-square goodness-of-fit example with my Western PA students at Gannon University this Fall. I even created a data file that mimics the findings (Methods:  n  = 380, Results: p < .001. Conclusion: Sheetz followers on Twitter love some curly fry). If you are a poor, unfortunate soul who has never enjoyed treatz from Sheetz, I feel bad for you. Look up your favorite regional brands on Twitter and translate one of their polls into a chi-square example. Or travel to your nearest Sheetz to experience some damn joy. 

YouGov America's Thanksgiving-themed chi-square examples

YouGov gifts us with seasonal chi-square examples  with data on Thanksgiving food controversies. For example: How do people feel about marshmallows on sweet potato dishes? This doesn't look randomly distributed to me. Which is more beloved: Light or dark turkey meat? If you want examples for the chi-square test of independence, dig into the PDF containing ALL of this survey's data. The distribution of people who like cranberry sauce by age group does not appear random.

Explaining chi-square is easier when your observed data equals 100 (here, the US Senate)

UPDATE: 2020 Data: https://www.catalyst.org/knowledge/women-government When I explain chi-square at a conceptual, no-software, no-formula level, I use the example of gender distribution within the US Senate. There are 100 Senators, so the raw observed data count is the same as the observed data expressed via proportions. I think it makes it easier for junior statisticians to wrap their brains around chi-square.  I  usually start with an Goodness-of-Fit (or, as I like to call them, "One-sies chi-squares").For this example, I divide senators into two groups: men and women. And what do you get?  For the 115th Congress, there are 23 women and 77 men . There is your observed data, both as a raw count or as a proportion. What is your expected data? A 50/50 breakdown...which would also be 50 men and 50 women. Without doing the actual analysis, it is pretty safe to assume that, due to the great difference between expected and observed values, your chi-square Goodness o...

Chi-square via The Onion's "Saying ‘Smells Okay’ Precedes 85% Of Foodborne Illnesses Annually"

Once again, The Onion publishes satire research (which should be, like, a submission category for JPSP) claiming to study phrases uttered before food poisoning happens . https://www.theonion.com/report-saying-smells-okay-precedes-85-of-foodborne-1819579726 I've turned this fake research into fake data to conduct an actual chi-square test of goodness of fit. Here is data that will give you a significant chi square, with 85% of participants falling into the "smells okay" category. Did sick person say aid "Smells Okay" before eating leftovers? No Yes 19 106