My friends. For you, I have compiled all of my funny statistic-Christmas images into one Google Slide presentation. You are welcome:  https://docs.google.com/presentation/d/12nmMw-69Ez71VmzaZ_QbUx4NOXiP17LHnvDjWUrx094/edit?usp=sharing

I don't find many examples of cluster analysis to share, but this example is REALLY engaging (using data to find serial killers), and is simple enough for a baby statistician BUT you can also make it a more advanced lesson as the data's owners freely share their data and code. Short Version: Journalist Thomas Hargrove (and his team) used cluster analysis to find clusters of similar killings within geographic areas. These might be a sign that a serial killer is active in that geographic region. It correctly identified a killer in Indiana. I found this interview from datainnovation.org which most succinctly describes the data analysis: https://www.datainnovation.org/2017/07/5-qs-for-thomas-hargrove-founder-of-the-murder-accountability-project/ Also statsy because the cluster analysis was validated using data from known serial killers. Hargrove's data and code can be accessed  here  and more information on his overall project to solve murders can be found...

UPDATE: 2020 Data: https://www.catalyst.org/knowledge/women-government When I explain chi-square at a conceptual, no-software, no-formula level, I use the example of gender distribution within the US Senate. There are 100 Senators, so the raw observed data count is the same as the observed data expressed via proportions. I think it makes it easier for junior statisticians to wrap their brains around chi-square.  I  usually start with an Goodness-of-Fit (or, as I like to call them, "One-sies chi-squares").For this example, I divide senators into two groups: men and women. And what do you get?  For the 115th Congress, there are 23 women and 77 men . There is your observed data, both as a raw count or as a proportion. What is your expected data? A 50/50 breakdown...which would also be 50 men and 50 women. Without doing the actual analysis, it is pretty safe to assume that, due to the great difference between expected and observed values, your chi-square Goodness o...

We already knew that model/cookbook author  Chrissy Teigen is really good at Twitter. We recently learned that, delightfully, she is also good at spotting misrepresented statistics. This came to light when she asked for help understanding the whole Jacob Wohl Debacle . She asked her Twitter followers for a clear, quick explanation of the whole deal. She didn't even @ Jacob, but Jacob got snippy and replied back with Google Trends data (how have I not blogged about Google Trends yet?) in an attempt to use data, beautiful data, in order to own Chrissy. And Chrissy was having none of it.  Yes, her sweet burn is an inspiration to us all, but it also a good demonstration of that fact that the exact same data can be interpreted in two different ways. And jerks lie with data, too, and can lie with actual, truthful data. And Chrissy knows her way around a chart.