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Showing posts with the label cognitive psychology

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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...

Why do post-partum women see faces everywhere?

Y'all. This is a statsy example featuring sensation and perception, developmental, and neuroscience.  The study found that post-partum, but not pregnant, women, saw faces where there were no faces (pareidolia illusion) . It is attributed to the endogenous oxytocin bump women experience after they have babies. Here is a link to Newsweek's treatment of the study and the actual study . Here are some examples of the photos used in the experiment. They are so dear because I see faces. I think my favorite is the clothes washer. Anyway, the researchers used pregnant women, post-partum women, and a control group and measured how often they saw faces. How to use 1. There is a good ol' Mann-Whitney U in this study. Making this the first ever Mann-Whitney U featured on the blog. 2. The researchers used OSF, and the data is available . 3. I like the growing trend of pairing newer and older data visualizations. Here, bar graphs and jitter plots are used to illustrate the same data, and...

One sample t-tests, puppies, real data.

This teaching example: 1. Is psychology research. 2. Features the actual data from the generous and helpful Dr. Bray . 3. Features GIFs. EVERYTHING is better with GIFs. 4. Includes puppies. 5. Includes a good ol' Psych Statistics standard: The one-sample t-test. Okay, get ready. I first learned about Dr. Emily Bray's dog cognition research via Twitter . Never let it be said that good things don't happen on Twitter. Occasionally.  1 Dogs are known for their ability to cooperate with humans and read our social cues. But are these skills biologically prepared? To find out, we tested 375 puppies at 8.5 weeks on 4 social cognition tasks (task descriptions: https://t.co/aETequNBce ) #AnimBehav2021 #Cognition pic.twitter.com/7vN2lp82Dp — Emily Bray (@DrEmilyBray) January 27, 2021 This is such a helpful way to share your research. This example works for your Cognitive or RM classes as well as your stats class, since this thread illustrates not just her findings but her methods. T...

Jon Mueller's Correlation or Causation website

If you teach social psychology, you are probably familiar with Dr. Jon Mueller's Resources for the Teaching of Social Psychology website .  You may not be as familiar with Mueller's Correlation or Causation website, which keeps a running list of news stories that summarize research findings and either treat correlation appropriately or suggest/imply/state a causal relationship between correlational variables. The news stories run the gamut from research about human development to political psychology to research on cognitive ability. When I've used this website in the past, I have allowed my students to pick a story of interest and discuss whether or not the journalist in question implied correlation or causation. Mueller also provides several ideas (both from him and from other professors) on how to use his list of news stories in the classroom.