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en_US Englishfr_FR French

Wondering if your test results are statistically significant?
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What Is an AB Testing Calculator?

An A/B testing calculator is a tool that helps to analyze the statistical significance of experiments comparing two versions (A and B) of a webpage or app feature. It calculates metrics like conversion rates to help determine whether changes have a significant impact on user behavior, aiding data-driven decision-making.

For example, if you make some changes to the homepage of your website, then the AB test calculator can analyze if the new changes have impacted the conversions adversely or improved them.

Calculate the Statistical Significance of Your Test

Put the respective values in the boxes below, and our AB testing calculator will calculate the statistical significance for you.

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Conversions

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Conversion Rate Limits Percentage

Conversion Rate

Standard Error

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Z-Score

P-Value

Significant at % Confidence

Conversion Rate Limits

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What is statistical significance?

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Statistical significance in A/B testing deals with the difference between the control version and the test version in your experiment, and that it is not due to error or random fate.

This is ensured by using a significance level. A significance level of 98% means that one can be 98% confident that any difference between your control version and the test version is real.

Why is statistical significance used?

Statistical significance comes in handy when a business wants to observe how a change in their product or service (the experiment) can affect their business. Is there a positive or negative brought on by that change, and if there is, then why?

Statistical significance is used to ensure that any data you collect falls well within the margin of error you have deemed acceptable (signified by a confidence level), and that any final data is not one prone to error.

How to calculate statistical significance?

The first step in A/B testing (or finding out statistical significance) is to formulate a hypothesis. There is a null hypothesis (H0, 0 being ‘naught’) and an alternative hypothesis. 

Typically, the null hypothesis states that there is no relationship between the variables you are comparing. The alternative hypothesis tries to prove a relationship exists and that the “test” is successful.

In A/B testing, there can be many instances that would work as a hypothesis – like adding a button on a website or an app, changing the UI or layout or color scheme, and testing if these changes are affecting conversion rates by showing some users the normal (control) version. 

A z-score is used to test the veracity of your null hypothesis. 

A p-value signifies the strength you have in favor of your hypothesis.

In A/B testing, you must also decide whether to conduct a one-tailed or two-tailed test. One-tailed tests can only account for directional effects from your alternative hypothesis. Two-tailed tests, on the other hand, also account for the eventuality that your hypothesis may have a negative impact. It is the safer approach.

Tips to improve your results with AB testing

Although A/B testing is an excellent technique for trying out changes and updates to your product, you need to conduct it the right way. There are a few techniques and guidelines you can keep in mind while conducting A/B tests, which are:

👉Increase sample size

The more people that partake in your tests, the more accurate the insights you will receive. For A/B testing, it means that you run your tests for a longer period of time, giving more people the chance to test out your null and alternative hypothesis.

👉Artificially direct traffic

You can add more links to your test pages on your social media and on your website to direct more traffic to them, allowing for a stress test of sorts.

👉Try significant changes

You can try making bigger changes to your product and testing its’ shock value on your users. This doesn’t mean a simple change to your color palette. Try getting users to engage with your services in an entirely new manner.

👉Don’t assume anything

Even if one scenario performs better than the other, it may not signify that users actually prefer using it. Therefore, you must also pair A/B testing with online surveys to get a deeper understanding of your users and determine whether your A/B testing has yielded actionable insights.

See how Voxco can help transform your survey research in 30 minutes.
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