Abstract
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.
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Abbreviations
- ANOVA:
-
Analysis of variance
- Arg:
-
l-Arginine
- DIO:
-
Diet-induced obese
- HF:
-
High fat
- LF:
-
Low fat
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Acknowledgments
This work is supported by grants from the National Cancer Institute (R25T-CA090301 and R37-CA057030), King Abdullah University of Science and Technology (KUS-CI-016-04; RJC), National Research Initiative Competitive Grants from the Animal Reproduction Program (2008-35203-19120) and Animal Growth and Nutrient Utilization Program (2008-35206-18764) of the USDA National Institute of Food and Agriculture, AHA (10GRNT4480020), and Texas AgriLife Research (H-8200).
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Wei, J., Carroll, R.J., Harden, K.K. et al. Comparisons of treatment means when factors do not interact in two-factorial studies. Amino Acids 42, 2031–2035 (2012). https://doi.org/10.1007/s00726-011-0924-0
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DOI: https://doi.org/10.1007/s00726-011-0924-0