Reassess thetTest: Interact with All Your Data via ANOVA

Abstract

Plant biology is rapidly entering an era where we have the ability to conduct intricate studies that investigate how a plant interacts with the entirety of its environment. This requires complex, large studies to measure how plant genotypes simultaneously interact with a diverse array of environmental stimuli. Successful interpretation of the results from these studies requires us to transition away from the traditional standard of conducting an array of pairwise t tests toward more general linear modeling structures, such as those provided by the extendable ANOVA framework. In this Perspective, we present arguments for making this transition and illustrate how it will help to avoid incorrect conclusions in factorial interaction studies (genotype 3 genotype, genotype 3 treatment, and treatment 3 treatment, or higher levels of interaction) that are becoming more prevalent in this new era of plant biology. IDENTIFYING BIOLOGICAL INTERACTIONS BETWEEN AND AMONG TREATMENTS, GENOTYPES, AND ENVIRONMENTS IS CRITICAL IN PLANT SCIENCETesting interactions between and among treatments, genotypes, and environments (Table 1) is central to nearly every field of plant biology, from genetics tests for epistasis, to physiology tests for interactions of multiple treatments. Understanding how results translate from one condition to another requires us to determine how these variables interact with each other in the context of an experiment. These interactions between variables form the basis of integrative studies that aim to assess how genetic variation influences the response to a specific treatment or environment. As a result, numerous plant biology studies require robust statistical methods to test hypotheses about how two variables interact. OVERUSE OF STUDENT'S t TESTSGiven the ubiquity of testing interactions, plant biologists are naturally well versed in the importance of assessing their data for statistical significance. Unfortunately, the analysis methods used are not always appropriate. A survey of three recent issues of The Plant Journal (Vol. 81, issues 1 to 3), The Plant Cell (Vol. 26, issues 10 to 12), and Plant Physiology (Vol. 166, issue 4, and Vol. 167, issues 1 and 2) showed that 83 of 185 articles (45%) relied solely upon pairwise t tests using a single trial of an experiment to analyze quantitative data, with the vast majority of studies involving multiple variables, experiments, or interactions. Of the remaining articles, 42 (23%) presented no quantitative data relevant to the statistics discussed in this article (i.e., modeling results or developmental pictures) and 41 (22%) reported quantitative data for which statistical analysis was either not conducted or not described. Finally, only 19 (10%) combined the data from multiple trials within an ANOVA to directly test for an interaction between two variables. Although we do not suggest that ANOVA would have been the best option in all of the above instances, we feel that these numbers indicate the extent to which ANOVA is underutilized in our community.

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