Ready for some Logic 101? Something real easy, I promise. We’ll use it in service to show why falsificationism is not that interesting, or useful, and we’ll need it in judging how […]
What Is A Model? We need to know to test between good and bad science
Listen to the podcast at YouTube, BitChute, and Gab. Before we describe what models are in science, it’s best to know, and to never forget, that all models only say what they […]
All Those Warnings About Models Are True: Researchers Given Same Data Come To Huge Number Of Conflicting Findings
Seventy-some researcher groups were given identical data, and asked to investigate an identical question. The groups did not communicate. Details are in the paper “Observing Many Researchers Using the Same Data and […]
How Can A Man Who Loses A Race Be Called The Winner & What Does This Have To Do With Science?
In which we start simple, stay simple, come to Plateau Easy, and the readers begins to wonder why he bothers, which he discovers at the end to his puzzlement. Get In Line […]
Solved: The Best Bayesian Prior To Use In Every Situation
Kevin Gray is back with another question, this time about priors. His last led to the post “Was Fisher Wrong? Whether Or Not Statistical Models Are Needed.” (The answer was yes and […]
The Multiverse Hides The Problem It Was Supposed To Solve, And Calls It Solved
Let’s end the review of Sabine Hossenfelder’s book. We did some already: Emergence, Entropy, and Many Worlds. With this post, we’ll have covered the most interesting topics. Old Hoss, and many like […]
How Not To Think Like A Bayesian Rationalist
Lisping Rationalists When I read people like Eliezer Yudkowsky and Scott Alexander it becomes clear to me how the French could have built a Temple of Reason during The Terror. Constructed, you […]
More Proof Hypothesis Testing Is Wrong & Why The Predictive Method Is The Only Sane Way To Do Statistics
Here it is, friends, the one complete universal simple function, the only function you will ever need to fit any—I said any—dataset x. And all it takes is one—I said one—parameter! . […]
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