Is it possible to p-hack with plots?

Most people think plots are for exploration and models are for testing. But that divide comes from a misunderstanding of the writings of John Tukey. In this video, I show how statistical models can give precise answers to the wrong question and how plots can reveal when that’s happening. Using concrete examples, we’ll see how model assumptions (like linearity or averaging across groups) can hide important structure, even when the model looks “statistically significant.” Plots don’t replace models. They test them. Paper about history of objectivity bias: https://osf.io/preprints/psyarxiv/q9d28 For the self-guided simplistics course: https://simplistics.net/course/univar... For the self-guided visualization course: https://simplistics.net/course/random... For the self-guided Mixed Models course: https://simplistics.net/course/mixed/ For the self-guided R course: https://simplistics.net/course/introd... For other classes, see: https://simplistics.net/all-courses/ For consulting, see: https://simplistics.net/product/stati...