Applying guidelines to construct informative priors in small sample research
The current paper demonstrates the usefulness of Bayesian estimation with small samples. In Bayesian estimation, prior information can be included. Prior information is information about the model parameters that originates from sources other than the data at hand. These sources can be literature, experts, or general knowledge. Including prior information increases the precision of the posterior distribution. The posterior distribution reflects what values are likely given the current state of knowledge, irrespective of the size of the current sample. Null hypothesis significance testing (NHST), on the other hand, suffers from low power with small samples, which often renders non-significant p-values that are difficult to interpret. An issue that received little attention in previous research, however, is the proper acquisition of prior information. The current study provides a set of general guidelines for collecting prior knowledge and formalizing it in prior distributions. Moreover, the current study also demonstrates how prior knowledge can be acquired systematically with an empirical application about development of working memory in young heavy cannabis users and non-using peers. To collect prior information, meta-analyses, reviews, empirical papers, experts, and general knowledge were involved. The paper closes with a discussion that also warns against the misuse of prior information.