BACKGROUND: The robustness of F-test to non-normality has been studied from the 1930s through to the present day. However, this extensive body of research has yielded contradictory results, there being evidence both for and against its robustness. This study provides a systematic examination of F-test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences. METHOD: We conducted a Monte Carlo simulation study involving a design with three groups and several known and unknown distributions. The manipulated variables were: Equal and unequal group sample sizes; group sample size and total sample size; coefficient of sample size variation; shape of the distribution and equal or unequal shapes of the group distributions; and pairing of group size with the degree of contamination in the distribution. RESULTS: The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions.
BACKGROUND: The robustness of F-test to non-normality has been studied from the 1930s through to the present day. However, this extensive body of research has yielded contradictory results, there being evidence both for and against its robustness. This study provides a systematic examination of F-test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences. METHOD: We conducted a Monte Carlo simulation study involving a design with three groups and several known and unknown distributions. The manipulated variables were: Equal and unequal group sample sizes; group sample size and total sample size; coefficient of sample size variation; shape of the distribution and equal or unequal shapes of the group distributions; and pairing of group size with the degree of contamination in the distribution. RESULTS: The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions.
Authors: Prabarna Ganguly; Jennifer A Honeycutt; June R Rowe; Camila Demaestri; Heather C Brenhouse Journal: Brain Behav Immun Date: 2019-01-14 Impact factor: 7.217
Authors: Garrett C McCarthy; Sydney C Morgan; Jonathan T Martiniuk; Brianne L Newman; Stephanie E McCann; Vivien Measday; Daniel M Durall Journal: PLoS One Date: 2021-02-04 Impact factor: 3.240
Authors: Laura L Bischoff; Thomas Cordes; Charlotte Meixner; Daniel Schoene; Claudia Voelcker-Rehage; Bettina Wollesen Journal: Aging Clin Exp Res Date: 2020-06-14 Impact factor: 3.636