Overcoming the pitfalls of categorizing continuous variables in ecology, evolution, and behavior

Many variables in biological research - from body size to life history
timing to environmental characteristics - are measured continuously (e.g.,
body mass in kilograms) but analyzed as categories (e.g., large versus
small), which can lower statistical power and change interpretation. We
conducted a mini-review of 72 recent publications in six popular ecology,
evolution, and behavior journals to quantify the prevalence of
categorization. We then summarized commonly categorized metrics and
simulated a dataset to demonstrate the drawbacks of categorization using
common variables and realistic examples. We show that categorizing
continuous variables is common (31% of publications reviewed). We also
underscore that predictor variables can and should be collected and
analyzed continuously. Finally, we provide recommendations on how to keep
variables continuous throughout the entire scientific process. Together,
these pieces comprise an actionable guide to increasing statistical power
and facilitating large synthesis studies by simply leaving continuous
variables alone. Overcoming the pitfalls of categorizing continuous
variables will allow ecologists, ethologists, and evolutionary biologists
to continue making trustworthy conclusions about natural processes, along
with predictions about their responses to climate change and other
environmental contexts.