Sequential A/B Testing: Workflow and Advantages over Classic Experiments

Comparing classic and sequential A/B testing

When it comes to A/B tests, anyone has a natural desire to get trustworthy results without spending a heap of money on traffic. Alas, it’s not always possible with classic A/B testing which requires enormous sample sizes at times. 

Is there a better way? Sure, there is!

Sequential A/B testing might become a robust alternative. Such experiments don’t only optimize necessary traffic volumes but also reduce the likelihood of mistakes. Let’s take a closer look at this method and how it differs from the classic A/B testing flow.
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Determining A/B Testing Sample Size: Method Based on Statistical Hypothesis Testing

Sample Size for mobile A/B testing

One of the most popular questions app publishers ask is how much traffic they need to run valid A/B tests. Unfortunately, there is no answer with a magic number that will fit every single experiment. An optimal traffic volume for mobile A/B testing is individual and depends on such factors as a traffic source, app’s conversion rate, and targeting.

Now let’s get to the main point: how to determine the sample size for A/B tests? It’s really important to have a full understanding of it as sample size has a considerable effect on checking the significance of the observed difference in variations performance.

In this post, we’ll also review one of the A/B test sample size measuring methods which is widely used and helps to make a statistically valid decision based on the results of your mobile A/B testing.
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