Tag
normality
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Should you check normality of the outcome variable or of the residuals?
Linear models assume normal errors, not a normal outcome. A simulation shows how checking Y instead of the residuals misleads in both directions.
Read the answer →t test or Mann-Whitney in small samples: how do you choose?
Why a normality pre-check is a weak way to pick a t test or a rank test with small n, what a power simulation shows, and where permutation tests fit.
Read the answer →t test vs ANOVA with two groups: are the assumptions really different?
Why a pooled t test and a one-way ANOVA on two groups give the same p value, and why their normality assumptions are the same thing stated two ways.
Read the answer →Does a t test need a minimum sample size?
Why no fixed minimum n makes a t test valid, what small samples really cost (power and robustness), and how to justify your sample size.
Read the answer →What does a normal Q-Q plot's shape actually tell you?
A normal Q-Q plot compares your data to a normal distribution. The shape of the departure from the line tells you what kind of departure you have.
Read the answer →Is a normality test worth running on your data?
Normality tests answer the wrong question: with lots of data they flag harmless quirks, with little data they miss real problems. Here is what to do.
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