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...
Your students have heard of probiotics. In pill form, in yogurt, and if you are a psychology major, there is even rumbling that probitotics and gut health are linked to mental health.
But this is still an emerging area of research. And NPR did a news story about a clinical trial that seeks to understand how probiotics may or may not help eliminate GI problems in children who are on antibiotics. Ask any parent, and they can tell you how antibiotics, which are wonderful, can mess with a kid's belly. When they are already sick.
Science is trying to provide some insight into the health benefits of probiotics in this specific situation. They spell out the methodology:
How to use in class:
1) I love about this example is that the research is happening now, and very officially as an FDA clinical trial. So talk to your students about clinical trials, which I think you can then related back to why it is good to pre-register your non-FDA research, with explicit research methodology, outcome measures, etc.
2) I think this example also illustrates the slow, iterative process of science. Sure, other research has investigated probiotics. But this research is specifically investigating children, using a specific probiotic, and measuring very specific outcomes, like instances of GI problems, fecal samples, and pediatric quality of life.
3) You could even use this to explain an independent t-test. Two groups of kids, one gets the yogurt with probiotics, other group gets placebo yogurt. And then they measure something at the end. Clearly, the actual research isn't a t-test as their are multiple outcome measures, but it is still a conceptual example.
But this is still an emerging area of research. And NPR did a news story about a clinical trial that seeks to understand how probiotics may or may not help eliminate GI problems in children who are on antibiotics. Ask any parent, and they can tell you how antibiotics, which are wonderful, can mess with a kid's belly. When they are already sick.
Science is trying to provide some insight into the health benefits of probiotics in this specific situation. They spell out the methodology:
1) I love about this example is that the research is happening now, and very officially as an FDA clinical trial. So talk to your students about clinical trials, which I think you can then related back to why it is good to pre-register your non-FDA research, with explicit research methodology, outcome measures, etc.
2) I think this example also illustrates the slow, iterative process of science. Sure, other research has investigated probiotics. But this research is specifically investigating children, using a specific probiotic, and measuring very specific outcomes, like instances of GI problems, fecal samples, and pediatric quality of life.
3) You could even use this to explain an independent t-test. Two groups of kids, one gets the yogurt with probiotics, other group gets placebo yogurt. And then they measure something at the end. Clearly, the actual research isn't a t-test as their are multiple outcome measures, but it is still a conceptual example.