Effect Size

Mack C. Shelley

In a very large sample almost any, even trivial, difference will be statistically significant. This happens because the standard errors for test statistics become smaller as sample size increases, which makes the test statistic larger (because the standard error is the denominator), and hence has a smaller p value. Effect size is about the distinction between statistical and practical significance, which gets complicated because effect size can be calculated for many different situations and for all levels of data (nominal, ordinal, interval). One such measure is the Pearson product-moment correlation (r), ranging from–1 to +1, where a value of–1 means that two variables are perfectly inversely related, +1 means that two variables are perfectly directly related, and 0 means that there is no relationship between two variables. When r is squared, it is the coefficient of determination, measuring the proportion of variation in the dependent variable explained by one or ...

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