Skip to main content

Posts

Showing posts from September, 2025

Latest Post:

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...

Percentiles, bee swarm plots, Bureau of Labor Statistics data...so many lessons in one interactive chart.

 There are so many ways to use this tool: Nathan Yau's Flowing Data is one of those websites I check every few days for statistical inspiration. He shares  the work of others and his own, including this  interactive bee swarm plot that illustrates salaries  for various  jobs. The bee plot, with the cursor of Psychology Teachers. https://flowingdata.com/2025/09/09/salary-and-occupation-2024/ There are many ways to use this in stats class: 1. At some point, you should talk about career exploration with your students.   2. Statistics students should be learning about modern data visualizations like this jitter plot, aka bee swarm plot.  3. If you cursor over any dot, you can see the 25th and 75th percentile scores and n size for that occupation's salary. 4. The size of each circle corresponds to the n size. Which I love because jitter plots do a great job of illustrating variability in a data set. However, each data point here represents an average...

Teaspoons, Tablespoons, and a new analogy for family-wise error.

This blog post contains one small analogy for explaining family-wise error to your students. I was making French toast for dinner the other night.  While I was measuring out cinnamon, I realized using one tablespoon instead of three teaspoons to avoid measuring errors is sort of like using a one-way ANOVA with three levels instead of doing three  t  tests to avoid Type I error.   Stick with me here. If I were to use three teaspoons to measure out an ingredient, there is a chance I could make a mistake three times. Three opportunities for air pockets. Three opportunities to not perfectly level out my ingredient. Meanwhile, if I just use one tablespoon, I will only risk the error associated with using a measuring spoon once.  Similarly, every time we use NHST, we accept 5% Type I error (well, if you are a psychologist and using the 5% gold standard, but I digress). Using three tests ( t tests) when we could use one (ANOVA) will increase the risk of a false positi...