I think there is not better resource out there to educate people about the misconception that there is no effect when p>. 05 then my textbook. The chapter on equivalence testing, lakens.github.io/statistical_... And if needed, chapter 1 on p-values, and perhaps even the section on bayes factors.
9 Equivalence Testing and Interval Hypotheses – Improving Your Statistical Inferences
This open educational resource contains information to improve statistical inferences, design better experiments, and report scientific research more transparently.
lakens.github.io
What explainer/resource do you direct researchers to when they conclude the null hypothesis is true on the basis of not rejecting it? It ought to mention that possible solutions include equivalence testing (or just interpreting the confidence interval) and Bayes factors. Maybe just "ask AI".