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...

As described in Wired's pop piece, the average body temperature for healthy adults isn't 98.6℉. Instead, data suggests that it is 97.7℉. Here is a link to the original study by  Hausmann, Berna, Ggujral, Ayubi, Howekins, Brownstein, & Dedeoglu . 1. This is an excellent theoretical example for explaining a situation where a one-sample t-test could answer your research question. 2. I created fake data that jive with the results, so you can conduct the test with your students. This data set mimicked the original findings for healthy adults (M = 97.7, SD = .72) and was generated with Andy Luttrell's Data Generator for Teaching Statistics . 97.39 97.45 97.96 97.35 96.74 99.66 98.21 99.02 96.78 97.70 96.90 97.29 97.99 97.73 98.18 97.78 97.17 97.34 97.56 98.13 97.77 97.07 97.13 9...

In the past, I've used fake data based on real research to create stats class examples. Baby names, NICUs, and paired t-test . Pain, surgical recovery, and ANOVA . Today, I've decided to use fake data and fake research to create a real example for teaching one-sample t-test. It uses  this research report from The Onion: https://www.theonion.com/toddler-scientists-finally-determine-number-of-peas-tha-1820347088 In this press release, the baby scientists claim that the belief that a baby could only smash four peas into their ear canal were false. Based upon new research recommendations, that number has been revised to six. Which sure sounds like a one-sample t-test to me. Four is the mu assumed true based upon previous baby ear research. And the sample data had a mean of 6, and this was statistically significant. Here is some dummy data that I created that replicates these findings, when mu/test value is set to 4. : 5.00 6.00 7.00 6.00 5.00 6.00 6.00 5.0...

UPDATE: 9/22: Sex ratio in India is normalizing: https://www.pewresearch.org/religion/2022/08/23/indias-sex-ratio-at-birth-begins-to-normalize/ I use this story from The Economist as a conceptual explanation of the one-sample t-test.  TL:DR: Sex ratio disparity data out of India is an abstract introduction to the one-sample t -test. So, at its most basic, one sample t -test uses some given, presumably true number/mu and tests your sample against that number. This conceptual example illustrates this via the naturally occurring sex ratio in humans (your mu) versus 2006-8 sex ratio data from different states in India (your sample data). Why look at this data? Social pressure, like dowries, high rates of sexual violence against women in India, etc., make male offspring more attractive than female offspring to some families. And the data provides evidence that this is leading to disturbing demographic shifts. For example, see the table below from The Economist: http://www.ec...