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Why would you multiple Cohen's d by sqrt(2) to get a "generic d"? 🤷‍♂️

See here:
.. the sample mean was 48.19, the expected age in the population was 50, so the difference would be 48.19 - 50 = -1.81. The standard deviation was 17.69, so Cohen's d becomes: d = -1.81 / 17.69 = 0.10. Cohen suggests to multiply this with the square root of 2, to get a generic d...  (Cohen, 1988, p. 40).

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🔥 take: journals asking researchers to report p-values exactly (e.g., p = 0.032) has led to more people misinterpreting Neyman-Pearson Hypothesis Testing, and believing p-values are strength of evidence.

(instead of just reporting p < 0.05)

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Do you know what Type M error and Type S error are?

(see Gelman & Carlin, 2014)

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What is the best way to "correct" for optional stopping (data peeking)?

I understand if I plan for a sequential design how to control the alpha, but what if someone comes to me saying "Oh, I looked at the data last week and the p-value was still big, I'm putting in for more funding to get more people"?

How do you correct the conditional probability?

Everyone focuses on misunderstandings of p-values, but the bigger problem is understanding effect sizes and power!

If you ran a properly powered study and were aware of a reasonable effect size in your research, then misinterpreting the p-value in your manuscript at the end will affect the scientific field less, as others can correct the interpretation later.

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p-values higher than your alpha (say, 5%) are not to be taken as "evidence of no effect". Everyone with me so far?

OK, now explain why this is exactly how almost all mediation analyses, multivariate regression models, SEM, fMRI papers, etc. use them! 🤷‍♂️

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Fantastic paper describes the bias that may arise *from* #regularisation in a multi level model.

https://www.cambridge.org/core/journals/political-analysis/article/understanding-choosing-and-unifying-multilevel-and-fixed-effect-approaches/8101D49CFD3B129F5753FC878F416980

Simple correction the authors propose only works if there is *no* treatment-confounder feedback(see: https://go-bayes.github.io/b-causal/posts/confounder-treatment-feedback/conf-out-feedback.html)

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