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#Sequential testing ab testing driver#

The data in this analysis comes from a sample of 1,001 tests conducted since the launch of the new Analytics Toolkit platform in late 2021. To examine the extent to which the Analytics Toolkit test planning and analysis wizard may encourage best practices in A/B testing and mitigate the issue of underpowered tests.To uncover new insights about key numbers such as test duration, sample size, confidence thresholds, test power, and to explore the distribution of effects of the tested interventions.

To check the real-world performance of sequential testing which by its nature depends on the unknown true effects of the tested interventions.

To produce a more powerful, and therefore more informative analysis.

To provide an outcome-unbiased analysis, improving on the previous study which likely suffered from selective reporting issues.

In light of the above, the current meta analysis has a number of goals: Other goals of the overhaul include prevention or minimization of other common mistakes in applying statistical methods in online A/B tests. In late 2021 an overhauled platform was released with one aim being to address the second of the major contributors to the poor outcomes of A/B testing efforts – inadequate statistical power. In 2017 a sequential testing methodology (AGILE) was proposed and implemented to address the motivations behind peeking in a way that provides efficiency and improves the ROI of testing without compromises to statistical rigor.

#Sequential testing ab testing driver#

The first can result in inflated estimates and lack of control of the false positive rate, whereas the second can result in failure to detect true improvements and missed opportunities to learn from tests due to underwhelming sample sizes.Īddressing such issues and promoting robust statistical practices has been a major driver behind the development of the A/B testing statistical tools at Analytics Toolkit since its launch in 2014. Namely, the majority of tests (70%) appeared underpowered, raising questions related to both unaccounted peeking and to low statistical power. A 2018 meta analysis of 115 publicly available A/B tests revealed significant issues related to the planning and analysis of online controlled experiments. Given this gatekeeper role, it is crucial that A/B tests are conducted in a way which results in robust findings while balancing the business risks and rewards from both false positive and false negative outcomes. As such, the role of A/B tests is primarily as a tool for managing business risk while addressing the constant pressure to innovate and improve a product or service. They are the preferred tool for estimation of the causal effects of different types of interventions, typically with the goal of improving the performance of a website or app, and ultimately of business outcomes. online controlled experiments are the gold standard of evidence and risk management in online business. Background and motivationĪ/B tests, a.k.a. Those interested in just the main findings and a brief overview can jump straight to “Takeaways”. The layout of the presentation is as follows: This meta-analysis of 1,001 A/B tests analyzed using the statistical analysis platform aims to provide answers to these and other questions related to online A/B testing. How long does a typical A/B test run for? What percentage of A/B tests result in a ‘winner’? What is the average lift achieved in online controlled experiments? How good are top conversion rate optimization specialists at coming up with impactful interventions for websites and mobile apps?