Talk:Cohen's h

Sample size calculation
I have notes on sample size calculation, but not coherent enough to put in the article.
 * For any given significance level, statistical power, and h, we can find the sample size. However, knowing significance level, statistical power, and a difference of proportions ($$p_1 - p_2$$) does not allow calculating a sample size.
 * In R, the sample size calculation can be done using the  function in the   package.
 * The usual sample size calculations assume that both $$p_1$$ and $$p_2$$ are estimated from the data. If, however, $$p_2$$ is known, then h is equal to $$\sqrt{2}$$ times the above definition of h. In other words, in this case,


 * $$h = \sqrt{2} \left( \phi_1 - \phi_2 \right)$$

Stats43 (talk)

Why is Cohen's h sensible?
Cohen's h is the Fisher information distance between two probabilities (see, e.g., Rao, 1987, Differential Metrics in Probability spaces), ie it is invariant to reparameterization. It doesn't become clear to me from this article what advantages this distance has compared to, say, the absolute differences between two probabilities. Biker333 (talk) 11:15, 28 May 2016 (UTC)


 * I found the paper you referred to here, but can't see anything about the arcsine transformation. Could you expand on this comment? Lordgrenville (talk) 21:04, 10 February 2022 (UTC)