Nonsignificant results are often incorrectly taken to reduce our confidence in the theory that predicted a difference. This is, however, a misinterpretation, as a nonsignificant result may be evidence for the null, evidence against the null, or an artefact of data insensitivity (Dienes, 2008, 2015). One way to overcome this issue is to use Bayesian inference techniques to quantify the evidence of H0 . In this webinar, I hope to illustrate how computing prior-informed BF analyses (following the approach advocated by Dienes, 2008) can be useful for literacy laboratory research, where BF applications have been scarce. I give examples from learning experiments with artificial lexicons investigating (1) children’s ability to extract statistical patterns from input presented under incidental and explicit conditions and (2) the relationship between statistical/explicit learning and literacy acquisition and discuss implications for other areas of literacy research.