![]() Your marketing team’s quest for exact predictions about future visitors and the inherent uncertainty in making such predictions necessitates statistical significance. A good example of this is Germany based company that improved their conversion rate by 33% by A/B testing their user experience. A/B testing allows you to battle the aforementioned uncertainty and improve your website’s user experience provided each and every step is planned considering each variable in play like total website traffic, sample traffic, test duration and so on. All you can do is observe the next few visitors, record their behavior, statistically analyze it, and based on that, suggest and make changes to optimize the experience of the next users. You cannot tell for certain how future visitors will react to your website. Why is statistical significance important?įor A/B testing to be successful, the test results should be statistically significant. Moreover, 80% significance also reflects that there is a 20% chance that you may be wrong. Your significance level also reflects your confidence level as well as risk tolerance.įor instance, if you run an A/B test with 80% significance, while determining the winner you can be 80% confident that the results produced are not a product of any random hunch or chance. In this data backed process, you first gather user insights on how they are interacting with your website and then use the gathered data to formulate a scientific testing hypothesis. Statistical significance is nothing but the probability that the gap between conversion rates of any chosen variation and the control is not because of random chance but due to a well planned, data-backed process. The ‘what’, ‘why’ and ‘how’ of statistical significanceīefore we move to complex statistical significance formulas, let’s first understand what it is, why it is important, and how to ensure that your tests conclude with statistical significance.įor A/B testing to be successful, the test results should be statistically significant. So, unless you believe in predicting A/B test results using Indian astrology, this blog will tell you the math behind calculating the statistical significance of your tests. ![]() However, A/B tests can be tricky to execute and interpret. ![]() VWO’s A/B testing solution helped retail company Greene improve their revenue by almost 60%. It talks about the math that goes behind A/B split testing and the importance of statistical significance. The article, on the other hand, provides an introduction to A/B testing statistics but like the testing calculator, does not talk about real formulas. The calculator provides an interface for you to calculate your A/B test’s statistical significance but does not give you the formulas used for calculating it. Earlier, we had published an article on the mathematics of A/B testing and we also have a free A/B test significance calculator on our website to check if your results are significant or not. The statistics of A/B testing results can be confusing unless you know the exact formulas.
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