In my intro stats class, we discuss shady data practices that aren't lying because they report actual numbers. But they are still shady because good data is presented in such a way as to be misleading or confusing. These topics include: Truncating the y-axis   Collecting measures of central tendency under ideal circumstances Manipulate online ratings (I didn't write the blog post about this yet, but it is coming). Relative vs. Absolute Risk AND HERE IS ANOTHER ONE: Insurance companies were asked to provide price data  RE: the Transparency in Coverage Rule in the Consolidatedated Appropriations Act of 2021. Google that if you want to know more about that, I'm not going into that. Not my lane. That said, it is an appealing idea. Let's have some transparency in our jacked-up healthcare system. And the insurance companies provided the data, but in a way inaccessible to most people. Like, all people, maybe? Because they just splurted out 100 TB of data. So, they totally com...

 Here is a quick RM review I created for my Psych Stats students. We were preparing for the first exam, which covered the very basics of research methodology, including IVs and DVs. We also talk about data visualizations and how they can be used to quickly convey information.  California is dealing with an energy crisis and a heatwave. California tried a relatively inexpensive intervention to reduce the likelihood of overwhelming the energy grid: Sending out text messages during extremely high energy usage. See:   https://www.bloomberg.com/news/articles/2022-09-07/a-text-alert-may-have-saved-california-from-power-blackouts And what happened? People reduced their electric usage. Source: https://www.bloomberg.com/news/articles/2022-09-07/a-text-alert-may-have-saved-california-from-power-blackouts For the class review, I asked my students to think of the emergency alerts they receive from their university via our campus safety app. I challenged them to think of a c...

This is a funny, small example for anyone who discusses managing missing data in a database. This example also touches on what can go wrong when using someone else's data or you merge datasets. So, this piece of information made the rounds in August: This isn't a lie. The voter rolls in Racine had over 20,000 voters with the same phone number. Which led to measured responses from voting rights experts on Twitter. Redhibiscus was so close to the truth! I assure you, if you have ever dealt with complicated databases, especially those that have been merged and go back decades, it isn't unusual to fill in missing data with a specific number repeatedly. Here is a fact check from the A.P. : https://apnews.com/article/fact-checking-612360682016?utm_source=Twitter&utm_campaign=SocialFlow&utm_medium=APFactCheck This isn't a big lesson for a statistics class, but it is a funny and horrifying example of how database management practices fueled a conspiracy theory. It is al...