Saturday, March 22, 2014

About Normality and Testing for Normality

It is often said that with small sample sizes, everything looks normal, as the normality tests are, indeed, very sensitive to what goes on in the extreme tails. In other words, if we have enough data to fail a normality test, we always will because our real-world data won’t be clean enough. If we don’t have enough data to reliably fail a normality test, then there’s no point in performing the test, and we have to rely on the fat pencil test or our own understanding of the underlying processes. Read the detailed reasoning at: