![]() ![]() These simulations show that in cases where agility and speed of experimentation are important, such trade-offs can be highly beneficial. One trade-off is to decide on decision boundaries when peeking, based on the cost/benefit of stopping futile or promising experiments early. Waiting for longer before peeking reduces this effect but may still result in negative consequences These simulations confirm that early peeking leads to significant degradations in true type I errors in case of the null hypothesis being true. Bayesian AB testing is not immuned to peeking.Evan Miller: how not to run an AB test.Lucid Chart: the fatal flaw of ab test peeking.Merritt Aho: AB testing peeking deep dive.Peeking threshold boundaries: can we make early decisions when the p-values excede a certain threshold ?.Visual interpretation of the effect of peeking.The effect of early peeking: impast of frequency and time of peeking.Experiment setup via simulations: true power, sample size and type I error. ![]() Peeking is discussed extensively in blog posts and some academic research, the purpose of this notebook is to provide snippets of code that allow data analysts to reproduce their AB tests via simulations and measure the effect of peeking on data similar to their own. based on its p value, statistical significance, secondary metrics etc) before the target sample size and power are reached. Early peaking is loosely defined as the practice of checking and concluding the results of an AB test (i.e. This notebook simulates the impact of early peaking on the results of a conversion rate AB test. AB Testing: the impact of early peeking ΒΆ ![]()
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