Statistics, Machine Learning, Data Science, Financial Planning, Python.
running multiple t tests increases the likelihood of making a Type I error (i.e., rejecting the null hypothesis when in fact it is true). A one-way ANOVA fixes this problem by adjusting for the number of groups. The purpose of a one-way ANOVA is to divide up the variance in some dependent variable