Baseline conversion=3%, MDE=5%, sample size (per variation)= 204,493.Judge for yourself, sample size discrepancy might be truly dramatic: This aspect once again proves the importance of a strong hypothesis which potentially causes a greater difference in variations performance. The higher the expected conversion lift, the less traffic you need. As a rule, 5% significance level is used in mobile A/B testing. Put simply, it’s the percent of the time when a test proves your hypothesis when in reality this hypothesis is false. Significance level - the percent of the time the difference (MDE) will be detected, assuming it doesn’t exist.Normally, 80% is an optimal statistical power. Statistical power - the percent of the time when the minimum effect size is detected, assuming it exists.You calculate the sample size you need for a meaningful experiment with a specialized calculator using the above-mentioned variables and statistical power as well as significance level:.Minimum Detectable Effect (MDE) - the minimum expected conversion lift.Baseline conversion - the percentage which defines the current conversion rate of the page you want to test.You define a baseline conversion and Minimum Detectable Effect (MDE):.Its principles are quite straightforward: Classic A/B Testing Flow: Core Principles and Challengesīefore proceeding to sequential A/B testing, let’s spare time to brush up our understanding of a classic A/B test. Nor, let’s take a closer look at this method theoretically and learn how it differs from the classic A/B testing flow. If you feel like exploring how it works in our platform, read on SplitMetrics sequential A/B testing principles. Such experiments don’t only optimize necessary traffic volumes but also reduce the likelihood of mistakes. Sequential A/B testing might become a robust alternative. Alas, it’s not always possible with classic A/B testing which requires enormous sample sizes at times. When it comes to A/B tests, anyone has a natural desire to get trustworthy results without spending a heap of money on traffic.
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