Journal
ECOLOGY AND EVOLUTION
Volume 11, Issue 11, Pages 5762-5776Publisher
WILEY
DOI: 10.1002/ece3.7365
Keywords
hypothesis; mechanisms; multiple working hypotheses; prediction; scientific method
Categories
Funding
- National Science Foundation [NSF-DEB-1457837]
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The use of research hypotheses in ecology and evolution is low, stable, and not correlated with grant success or citation rates, potentially disincentivizing hypothesis formulation.
Research hypotheses have been a cornerstone of science since before Galileo. Many have argued that hypotheses (1) encourage discovery of mechanisms, and (2) reduce bias-both features that should increase transferability and reproducibility. However, we are entering a new era of big data and highly predictive models where some argue the hypothesis is outmoded. We hypothesized that hypothesis use has declined in ecology and evolution since the 1990s, given the substantial advancement of tools further facilitating descriptive, correlative research. Alternatively, hypothesis use may have become more frequent due to the strong recommendation by some journals and funding agencies that submissions have hypothesis statements. Using a detailed literature analysis (N = 268 articles), we found prevalence of hypotheses in eco-evo research is very low (6.7%-26%) and static from 1990-2015, a pattern mirrored in an extensive literature search (N = 302,558 articles). Our literature review also indicates that neither grant success nor citation rates were related to the inclusion of hypotheses, which may provide disincentive for hypothesis formulation. Here, we review common justifications for avoiding hypotheses and present new arguments based on benefits to the individual researcher. We argue that stating multiple alternative hypotheses increases research clarity and precision, and is more likely to address the mechanisms for observed patterns in nature. Although hypotheses are not always necessary, we expect their continued and increased use will help our fields move toward greater understanding, reproducibility, prediction, and effective conservation of nature.
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