*Note: This post has been recently updated.
The statistics of A/B testing results can be confusing unless you know the exact formulas. 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 calculate if your results are significant or not.
The calculator provides an interface for you to calculate your A/B test’s statistical significance but does not give you real formulas used for calculating it. 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. It talks about the math that goes behind A/B split testing and the importance of statistical significance.
This blog will tell you the math behind calculating the statistical significance of your tests.
‘What’ ‘Why’ and ‘How’ of Statistical Significance.
Before 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.
For A/B testing to be successful, the test results should be statistically significant.
What is Statistical Significance?
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. 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.
Your significance level also reflects your confidence level as well as risk tolerance.
For 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. Moreover, 80% significance also reflects that there is a likelihood of 20% that you may be wrong.
Why is Statistical Significance important?
For A/B testing to be successful, the test results should be statistically significant. You cannot tell for certain how future visitors will react to your website. 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. 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.
Your marketing team’s quest for exact predictions about future visitors and the inherent uncertainty in making such predictions necessitates statistical significance. Statistical significance is also important because it serves as a source of confidence and assures you that the changes you make do have a positive impact on your business goals.
How to ensure the Statistical Significance of a test?
Statistical significance depends on 2 variables:
- The number of visitors, i.e your sample size.
- The number of conversions for both control and variation(s).
To ensure that your A/B tests conclude with statistical significance, plan your testing program keeping both these variables in mind. Use our free A/B test significance calculator to know your test’s significance level.
Excel Sheet with A/B Testing Formulas
So, we have come up with a FREE spreadsheet which details exactly how to calculate statistical significance in an excel. You just need to provide the number of visitors and conversions for control and variations. The spreadsheet automatically calculates for you the significance, p-value, z-value and other relevant metrics for any kind of A/B split testing (including Adwords). And to add to our article on the mathematics of A/B testing and free significance calculator, this spreadsheet highlights the formulas that are used in calculating test result significance.
Please feel free to share the file with your friends and colleagues or post it on your blog and social media handles.
PS: By the way, if you want to do quick calculations, we have a version of this calculator hosted on Google Docs (please make a copy of the Google Doc sheet into your own account before you make any changes to it).
Update: Thanks to Jai (in the comments below), we had noticed a minor error in conversion rate range calculations (though significance results were unaffected). The error has been fixed in the latest version of the spreadsheet.