Quantitative exploratory research implies a flexible examination of a dataset with the purpose of finding patterns, associations, and interactions between variables to help formulate a hypothesis, which should be severely tested in a future confirmatory study. In many fields, including sport and exercise science, exploratory research is not openly reported, a practice that leads to serious problems. At the same time, exploration is a crucial step in scientific knowledge generation, and a substantial proportion of studies will be exploratory in nature, or include both confirmatory and exploratory analyses. Using a flowchart, we review how data are typically collected and used, and we distinguish exploratory from confirmatory studies by arguing that data-driven analyses, where the Type I and Type II error cannot be controlled, is what characterises exploratory research. We ask which factors increase the quality and value of exploratory analyses, and highlight large sample sizes, uncommon sample compositions, rigorous data collection, widely used measures, observing a logical and coherent pattern across multiple variables, and the potential for generating new research questions as the main factors. Finally, we provide guidelines for carrying out and transparently writing up an exploratory study.
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