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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...

One small, psychological ANOVA example you can use in class.

This is just a little one-way ANOVA with three levels. You can use it in class to assess, review, or teach the topic. It comes from the following article by Rivera-Chavez et al.


https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2843427


TL:DR- They gathered data and performed a one-way ANOVA that suggests that people with emerging psychosis have glutamate (a neurotransmitter) levels that are higher than both controls and folks who have schizophrenia diagnoses.

Even if you aren't an expert on this topic, JAMA's ready to explain the relevance of this study to your students:

Conclusions and Relevance These findings suggest that the differences in glutamate levels may indicate that these alterations occur primarily in the early stages of the disease. The results of this study have implications for resuming clinical trials with targeted metabotropic glutamate receptor 2/3 agonists, taking into account both the disease phase and the glutamatergic alterations measured by magnetic resonance imaging.

Text reads: Key Points Question What is the temporal nature of glutamate alterations at different stages of the schizophrenia spectrum as revealed by using proton magnetic resonance spectroscopy? Findings This cross-sectional study reports prefrontal glutamate levels in 83 never-medicated individuals with psychosis with varying durations of illness and 60 controls. There were significant elevations of glutamate level in individuals classified as having first-episode psychosis compared with both individuals with chronic schizophrenia and controls. Meaning These findings suggest that early-stage schizophrenia is associated with elevated prefrontal glutamate levels, making it a target for compounds that reduce glutamatergic transmission and therapeutic potential.

Reasons why I love this as an example for my novice psychological statisticians:

1. This data is related to psychology, a simple one-way ANOVA with three levels, and was recently published, making it a nice little refresh to my course content.

There are other analyses in the article, but here are the ANOVA results.

Glutamate levels differed among the 3 groups (F2,136 = 7.5; P = .001). Post hoc pairwise comparisons revealed higher glutamate levels in the FEP group compared with both the chronic schizophrenia group (P = .003; Cohen d = 0.69) and the control group (P = .008; Cohen d = 0.83). There were no significant differences in glutamate levels between the chronic schizophrenia group and the control group (P > .99). Higher glutamate levels were associated with lower verbal (ρ = −0.29; P = .04) and visual learning scores (ρ = −0.29; P = .04) in the FEP group.

2. I emphasize that my students learn how to read and write statistical findings, so here are a few of the questions I'll ask my students after they read the text I copied and pasted above:

-What is the factor? What are the levels?

-What was the overall p-value for the ANOVA?

-According to the post-hoc, what was pulling the significance? 

3. Data is presented with a jitter plot. I'm so over bar graphs. Show me the variability, participant by participant. I also like the brain image that shows the exact portion of the brain being studied. 

4. This data isn't WEIRD. It is from a team in Mexico with a sample drawn from a Mexican hospital.









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