Bayesian inference and the difference between evidence of absence and absence of evidence

Null results are central to accumulating knowledge in marketing, yet conventional null hypothesis significance testing (NHST) renders such results ambiguous. A nonsignificant p -value does not distinguish between data insufficiency and data that meaningfully support the absence of an effect. In this brief note we argue that Bayesian inference provides a useful framework for interpreting null findings in marketing analytics. By allowing probability distributions on unknown parameters and then comparing the predictive adequacy of null and alternative models, Bayesian methods allow researchers to distinguish between the evidence of absence and the absence of evidence. This note outlines how Bayes factors, posterior intervals, and interval null hypotheses can make null findings more informative whether those findings derive from laboratory experiments, field experiments or secondary data studies. We note that publication bias in favor of significant results is not just an issue for editors but is rather a problem across the full field of marketing since it impedes accumulation of knowledge. In fact, when null results are analyzed and reported in ways that quantify evidential strength, they become easier to interpret, easier to compare across studies, and more useful to both academic theory and managerial decision-making.