A new paper in Proceedings of the National Academy of Sciences finds that politicians who are averse to lying have lower reelection rates. If true, this finding has substantial implications for whether politicians with ambitions of getting (re)elected should lie or not. Accordingly, I found it extra relevant to read this manuscript carefully (in contrast […]
Category: statistics
Dark Data: Why What You Don’t Know Matters
I like data and I like books about data. Unsurprisingly, I found the book Dark Data: Why What You Don’t Know Matters interesting to read. The book is packed with fun and interesting examples of what data can and cannot tell us. For people who teach introductory statistics and are running low on examples, I […]
Confusing and misleading terms in psychology
I was reading a couple of articles with examples of terms in psychological research that are either confusing, ambiguous or misleading. The two articles are Fifty psychological and psychiatric terms to avoid: a list of inaccurate, misleading, misused, ambiguous, and logically confused words and phrases (Lilienfeld et al. 2015) and 50 Differences That Make a […]
Tidyverse resources on YouTube
I have been watching a lot of YouTube videos lately with people using tidyverse. These videos are not tutorials per se but rather demonstrations on how to wrangle and analyse data. These videos use a lot of dplyr and ggplot2 as well as packages associated with the tidyverse, e.g. tidytext. For the data, they often […]
Potpourri: Statistics #67
726. Computational Causal Inference at Netflix 727. Tools for Ex-post Survey Data Harmonization 728. How to pick more beautiful colors for your data visualizations 729. Shiny in Production: App and Database Syncing 730. Introduction to Causal Inference 731. An Illustration of Decision Trees and Random Forests with an Application to the 2016 Trump Vote 732. […]