4 Matching Annotations
  1. May 2019
    1. Multiple comparisons: It is not good practice to test for significant differences among pairs of group means unless the ANOVA suggests some such differences exist. Nevertheless, I admit it is tempting to take another look at the comparison of G1 with G3 (ignoring the existence of G2 and perhaps assuming normality), but then you should use a Welch t test to account for the differences in sample variances, and you should not make claims about the result unless the P-value is as low as .01 or .02. Looking at that difference more carefully might prompt a subsequent experiment.

      Test for significance among pairs when the overall f test is not significant.

  2. Apr 2019
    1. There are two tests that you can run that are applicable when the assumption of homogeneity of variances has been violated: (1) Welch or (2) Brown and Forsythe test. Alternatively, you could run a Kruskal-Wallis H Test. For most situations it has been shown that the Welch test is best. Both the Welch and Brown and Forsythe tests are available in SPSS Statistics (see our One-way ANOVA using SPSS Statistics guide).

      ANOVA is robust against violation of the assumption of equal variances, but...

    2. However, platykurtosis can have a profound effect when your group sizes are small. This leaves you with two options: (1) transform your data using various algorithms so that the shape of your distributions become normally distributed or (2) choose the nonparametric Kruskal-Wallis H Test which does not require the assumption of normality.

      ANOVA is robust against violation of normality, but...