Comparisons of treatment means when factors do not interact in two-factorial studies

• Published in: Amino acids Vol. 42; no. 5; pp. 2031 - 2035
• Main Authors: Wei, Jiawei, Carroll, Raymond J., Harden, Kathryn K., Wu, Guoyao
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.05.2012; Springer Nature B.V
• Subjects: Algorithms; Analytical Chemistry; Biochemical Engineering; Biochemistry; Biomedical and Life Sciences; Biomedical Research; Data Interpretation, Statistical; Humans; Life Sciences; Models, Theoretical; Neurobiology; Proteomics; Short Communication; Main effects; Factor level means; Multiple comparison; Two-factor studies; Interaction; Treatment means
• ISSN: 0939-4451; 1438-2199
• DOI: 10.1007/s00726-011-0924-0

Scientists in the fields of nutrition and other biological sciences often design factorial studies to test the hypotheses of interest and importance. In the case of two-factorial studies, it is widely recognized that the analysis of factor effects is generally based on treatment means when the interaction of the factors is statistically significant, and involves multiple comparisons of treatment means. However, when the two factors do not interact, a common understanding among biologists is that comparisons among treatment means cannot or should not be made. Here, we bring this misconception into the attention of researchers. Additionally, we indicate what kind of comparisons among the treatment means can be performed when there is a nonsignificant interaction among two factors. Such information should be useful in analyzing the experimental data and drawing meaningful conclusions.