01382nam a2200169 a 4500 20260901030414.0 250101s2019 xx o 000 0 eng d Quentin F. Gronau Informed Bayesian Inference for the A/B Test arXiv 2019 text online resource Booming in business and a staple analysis in medical trials, the A/B test assesses the effect of an intervention or treatment by comparing its success rate with that of a control condition. Across many practical applications, it is desirable that (1) evidence can be obtained in favor of the null hypothesis that the treatment is ineffective; (2) evidence can be monitored as the data accumulate; (3) expert prior knowledge can be taken into account. Most existing approaches do not fulfill these desiderata. Here we describe a Bayesian A/B procedure based on Kass and Vaidyanathan (1992) that allows one to monitor the evidence for the hypotheses that the treatment has either a positive effect, a negative effect, or, crucially, no effect. Furthermore, this approach enables one to incorporate expert knowledge about the relative prior plausibility of the rival hypotheses and about the expected si Open access — freely available to read. https://arxiv.org/pdf/1905.02068v6 Read the full paper (PDF) ERES 712 712 0 0 0 3 GEN MAIN MAIN SCICOMP 2026-09-01 0 YGE000973 2026-09-01 03:04:14 https://arxiv.org/pdf/1905.02068v6 2026-09-01 PAPER