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p-Hacking and False Discovery in A/B Testing by Ron Berman, Leonid Pekelis, Aisling Scott, Christophe Van den Bulte :: SSRN
We investigate whether online A/B experimenters "p-hack" by stopping their experiments based on the p-value of the treatment effect. Our data contains 2,101 commercial experiments in which experimenters can track the magnitude and significance level of the effect every day of the experiment. We use a regression discontinuity design to detect p-hacking, i.e., the causal effect of reaching a particular p-value on stopping behavior.

Experimenters indeed p-hack, especially for positive effects. Specifically, about 57% of experimenters p-hack when the experiment reaches 90% confidence. Furthermore, approximately 70% of the effects are truly null, and p-hacking increases the false discovery rate (FDR) from 33% to 42% among experiments p-hacked at 90% confidence. Assuming that false discoveries cause experimenters to stop exploring for more effective treatments, we estimate the expected cost of a false discovery to be a loss of 1.95% in lift, which corresponds to the 76th percentile of observed lifts.
testing  research  statistics  marketing 
5 hours ago by Chirael
Pinterest Ads: A Step-by-Step Guide To Your First Promoted Pins Campaign by
pinterest  marketing  from twitter
7 hours ago by WeRank
Weekend musing – A simple Kill Switch solution to the mess that is Facebook
"Facebook can't seem to catch a break in the court of public opinion but there may be a simple solution to its woes. Bring on the Kill Switch."
content  marketing  customer  service  management  regulation  social 
10 hours ago by jonerp
100+ Google SEO Success Factors, Ranked Ξ Zyppy
Les facteurs de classement de Google, classés par ordre.
marketing  webdev  guide 
15 hours ago by alssanro

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