How prior and p-value heuristics are used when interpreting data

Abstract

ABSTRACTScientific conclusions are based on the ways that researchers interpret data, a process that is shaped by psychological and cultural factors. When researchers use shortcuts known as heuristics to interpret data, it can sometimes lead to errors. To test the use of heuristics, we surveyed 623 researchers in biology and asked them to interpret scatterplots that showed ambiguous relationships, altering only the labels on the graphs. Our manipulations tested the use of two heuristics based on major statistical frameworks: (1) the strong prior heuristic, where a relationship is viewed as stronger if it is expecteda priori, following Bayesian statistics, and (2) the p-value heuristic, where a relationship is viewed as stronger if it is associated with a small p-value, following null hypothesis statistical testing. Our results show that both the strong prior and p-value heuristics are common. Surprisingly, the strong prior heuristic was more prevalent among inexperienced researchers, whereas its effect was diminished among the most experienced biologists in our survey. By contrast, we find that p-values cause researchers at all levels to report that an ambiguous graph shows a strong result. Together, these results suggest that experience in the sciences may diminish a researcher’s Bayesian intuitions, while reinforcing the use of p-values as a shortcut for effect size. Reform to data science training in STEM could help reduce researchers’ reliance on error-prone heuristics.Significance StatementScientific researchers must interpret data and statistical tests to draw conclusions. When researchers use shortcuts known as heuristics, it can sometimes lead to errors. To test how this occurs, we asked biologists to interpret graphs that showed an ambiguous relationship between two variables, and report whether the relationship was strong, weak, or absent. We altered features of the graph to test whether prior expectations or a statistic called the p-value could influence their interpretations. Our results indicate that both prior expectations and p-values can increase the probability that researchers will report that ambiguous data shows a strong result. These findings suggest that current training and research practices promote the use of error-prone shortcuts in decision-making.