Interpretation of P value

[R_cubed]

A similar question was asked a few months ago. You might benefit from reading my post in this thread, and studying the paper I linked to.

Suffice it to say – you are right. I will borrow a quote from Sander Greenland

Blockquote
“Statistics made easy is code for statistics done wrong.”

I prefer to think of low p-values as “sufficiently surprising” if there is no effect.

Appropriate statistical methods to derive a cut-off predictive values for a binary outcome

Blockquote I would appreciate if you can help me understand. Philosophically – not statistics or some algebraic expression – I am doing a little experiment with GLM, my data is fixed … I cannot change it and my formula for this is fixed. A subtle point is that your data are a considered a sample from a hypothetical distribution for statistical purposes. Your data was one subset of a multitude of possible ones. This is a critical point that will be important a bit later. Blockquote I get…

This is also a good thread, that discusses the correct and unambiguous description of frequentist results.

Language for communicating frequentist results about treatment effects

Editorial Notes As Sander Greenland has so well stated below, this initial draft was still too “dichotomous” in that it was written assuming we would have different language for “positive” vs. “negative” studies. This implies a threshold for “positive” which is what we’re trying to get away from. So I’m trying to develop more “positive/negative agnostic” language. One initial stab at this is below in the Generic Interpretation section. The examples demonstrating incorrect and confusing sta…