By some counts there are more than 70 effect size indexes. Some of them you will be familiar with (e.g., odds ratio, relative risk). Some double-up as test statistics (e.g., r, R^{2}). And others sound like planets from Star Trek (e.g., the Pillai-Bartlett V).

Most effect size indexes can be grouped into one of two families:

differences between groups, a.k.a the d family (e.g., risk difference, risk ratio, odds ratio, Cohen’s d, Glass’s delta, Hedges’ g, the probability of superiority)

measures of association, a.k.a. the r family (e.g., the correlation coefficient r, R^{2}, Spearman’s rho, Kendall’s tau, phi coefficient, Cramer’s V, Cohen’s f, η^{2})

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“The primary product of a research inquiry is one or more measures of effect size, not p values.”
~ Jacob Cohen

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“Statistical significance is the least interesting thing about the results. You should describe the results in terms of measures of magnitude – not just, does a treatment affect people, but how much does it affect them.”
~ Gene Glass