Can p-values read your mind? How about Bayesian Statistics?

In this video, I’m going to argue that a p-value isn’t just a property of your dataset...it’s a property of your dataset plus the rules you followed to get there. I’ll start with a Schrödinger-style setup where two researchers analyze the same data, run the same final model, and print the same p-value, yet one result is genuinely interpretable while the other is basically statistical cosplay, simply because one person preregistered and the other went hunting. That’s the piece that feels paradoxical and bumps into the intuition behind the likelihood principle: evidence should depend on what happened, not on what could have happened or what I was “willing to try.” I’ll contrast this with Bayesian thinking: Bayes changes the contract and handles some “peeking” issues differently, but it’s not mind-proof; if I try many models and only report the best-looking story, the same selection problem comes right back. Link to a paper about the likelihood principle: https://media.pluto.psy.uconn.edu/Die... To take my new February simplistics class, see: https://simplistics.net/course/simpli... For the self-guided Mixed Models course: https://simplistics.net/course/mixed/ For the self-guided visualization course: https://simplistics.net/course/random... For the self-guided simplistics course: https://simplistics.net/course/univar... 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...