000			01382nam a2200169 a 4500
005			20260901030414.0
008			250101s2019 xx o 000 0 eng d
100	1		_aQuentin F. Gronau
245	1	0	_aInformed Bayesian Inference for the A/B Test
264		1	_barXiv _c2019
336			_atext
338			_aonline resource
520			_aBooming 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
506	0		_aOpen access — freely available to read.
856	4	0	_uhttps://arxiv.org/pdf/1905.02068v6 _yRead the full paper (PDF)
942			_cERES
999			_c712 _d712