Hypothesis 2

To examine whether students of the high-alcohol group showed a stronger AB to alcohol cues than students in the low-alcohol group, we performed one-tailed independent t tests comparing students drinking low amounts of alcohol, with students drinking high amounts of alcohol. We examined group differences for the OOOT and OOOT-adapt separately. Per condition, two independent t tests were performed, one on attentional engagement and one on attentional disengagement. Given multiple comparisons per group (engagement and disengagement bias), for the one-tailed independent t tests, we used an adjusted α of 0.025, to reduce the likelihood of incorrectly rejecting the null hypothesis (i.e., making a Type I error).

To increase confidence in our results delivered by the t tests following the frequentist approach, we also reported results following the Bayesian approach. Therefore, Bayesian independent-samples t tests with Cauchy priors were calculated, which are set at the recommended default r = 0.707. BF₁₀, which quantifies the evidence for the alternative hypotheses over the null hypotheses, was reported. A Bayes factor of 1 is considered no evidence, between 1 and 3 anecdotal, between 3 and 10 moderate, between 10 and 30 strong, between 30 and 100 very strong, and more than 100 extreme evidence that the data are in line with the alternative hypothesis. Conversely, a Bayes factor between 1/3 and 1 will be considered anecdotal; between 1/3 and 1/10, moderate evidence; between 1/10 and 1/30 strong evidence; between 1/30 and 1/100, very strong evidence; and less than 1/100, extremely strong evidence that the data are more likely under the null hypothesis (Wagenmakers et al., 2017).