This example provides evidence of data funny business beyond psychology, shows why pre-registration is a good thing, AND uses a chi-square. Bonus points for being couched in medicine and prominently featuring randomized controlled trials (RCT). Basically, Kaplan and Irving's  research checked out the results for RCTs funded with grants from the National Heart, Lung and Blood Institute. See below for how they selected their studies: And what did they find? When folks started registering their outcomes, folks started to get fewer "beneficial" results. Which probably REALLY means that some of those previous "beneficial" results were not so beneficial, or the result of some data massaging. See below: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0132382           Another reason to love this example: It is a real life chi-square that is easy to understand! I feel like I don't have enough great chi-square examples in my lif...

Y'all. I love collecting examples of awesome cookies and cakes and cupcakes beautified with statistics and data and graphs. Here is my Pinterest Board. My goal is to SOMEDAY have the time and skills to make some of my own. Until then, I will gush over other peoples accomplishments:

Do you know what we need as statistic instructors? A) More chi-square examples that are b) rooted in Taco Bell condiments and c) are null. So here you go, as inspired by this tweet : This data did not achieve statistical significance, X^2 (3, N = 32) = 0.33, p = 0.95. The data suggests that these Taco Bell packets are randomly distributed. If you do this analysis by hand, here is your data: Diablo = 8, Hot = 9, Fire = 7,  Mild = 9. If you do this analysis via software, here is the .csv version of the data , here is the .jasp version of the data , and here is a version of the data you can just copy and paste. Sauce Diablo Diablo Diablo Diablo Diablo Diablo Diablo Diablo Fire Fire Fire Fire Fire Fire Fire Fire Fire Hot Hot Hot Hot Hot Hot Hot Mild Mild Mild Mild Mild Mild Mild ...

Stick with me here. I think this would be a great warm-up activity early in the semester. My boy Ace had a research hypothesis, operationalized his research, tried to collect data points using several test subjects, and measured his outcomes. Here is the original interview from  Draft Diamonds  and  Newsweek's story . 1) How did he operationalize his hypothesis? What was his IV? DV? 2) Did he use proper APA headers? Should APA style require the publication of pictures of crying researchers if their findings don't replicate? 3) This data could be analyzed using a repeated measure ANOVA. He had various members of his family throw a football as different PSIs and he measured how far the ball traveled and calculated mean for three attempts at each PSI. 4) His only participants were his mom, dad, and sister. So, this study is probably underpowered. 5) In this video from NBC news , Ace's dad describes how they came up with the research idea. Ace i...

Someone on Reddit posted this cool picture of a...contraption? I'll go with contraption. Anyway, it automatically generates a chart of the amount of sunlight per day by burning a log. A Twitter follower recognized this as a Campbell-Stokes recorder . This is beautiful art and data visualization from Hood-Glen Park in San Francisco. How to use in class: 1) Make a bunch of really dumb log arithm jokes. 2) A nice introduction to data visualization. Maybe this could be paired with more traditional sources of weather data. 3) Also makes me think of other naturally occurring charts: Also, while less pretty, think about all the data that is automatically created every time Google Maps identifies your location (and then warns everyone using Google Maps to avoid traffic slowdowns) or Netflix provides you with recommendations based on viewing habits. The Campbell-Stokes recorder could serve as a metaphorical segue into a discussion about all the automated data collectio...

Here is a YouTube video that summarizes some research findings . The researchers looked at Tweets in order to study how are focus and emotions change with our sleep/wake cycles. And the findings are interesting and not terribly surprising. Folks are mellow and rational in the morning and contemplate their mortality at 2 AM. Make money, get paid. And THIS is why I go to bed by 9 AM. I don't need to think about death at 2:20 AM. How to use in class: 1) Archival data (via Tweet) to explore human emotion. 2) What are the shortcomings of this sample method. To be sure, their data set is ENORMOUS, but how are Twitter users different from other people? Do your students think these findings would hold for people who work the night shift? 3) Go back to the original paper and look more closely at the findings: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197002 4) This data represents one of the ways that researchers collect real-time information ...

This story by Christine Aschwanden  from 538.com  describes the limitations of a JAMA article.   That JAMA article describes a research project that found signs of Chronic Traumatic Encephalopathy (CTE) in 110 out of 111 brains of former football players. How to use in stats and research methods: 1) It is research, y'all. 2) One of the big limitations of this paper comes from sampling. 3) The 538 article includes a number of thought experiments that grapple with the sampling distribution for all possible football players. 4) Possible measurement errors in CTE detection. 5) Discussion of replication using a longitudinal design and a control group. The research: The JAMA article details a study of 111 brains donated by the families deceased football players. They found evidence of CTE in 110 of the brains. Which sounds terrifying if you are a current football player, right? But does this actually mean that 110 out of 111 football players will develop CTE...