A/B Test Calculator for Statistical Significance

The number of visitors on this page was:
The number of overall conversions was:
Conversion rate
> 9%
> 12%

Your test results

Test "B" converted 33% better than Test "A". I am 99% certain that the changes in Test "B" will improve your conversion rate.

Your A/B test is statistically significant!

There are only two states in the professional life of a marketer:

  • Something works, but doesn't bring results - you need to do better.
  • Something works and brings results, but still wants to do better.

In both cases, the specialist must understand what is worth improving and whether the new version will be worse than the previous one. To check their theories, marketers use A/B-testing. Then they use the A/B testing significance calculator.

A/B-testing is a marketing method that allows one to check the effectiveness of hypotheses, compare two elements, and choose the most effective one among them. The essence of the test is that users will see different variants of web pages and, accordingly, react to them differently. The variant that shows the best results wins in the end.

You can test and improve anything on the website, for example:

  • Call to action (CTAs)
  • Forms of data collection
  • Illustrations
  • Web page structure
  • Content

You can go through endless variants of what you want to test. The goal of A/B testing is always the same: to increase conversion rates (ideally more sales), click rates, and audience engagement. To achieve any of these, you need to do A/B testing correctly and then use the A/B testing significance calculator.

How to Calculate A/B Testing Significance?

To complete the A/B tests, it is important to understand that the data obtained have been interpreted correctly. This is to make sure that the results are not random.

If you skip this step, there is a chance that decisions will have to be made unreasonably. After all, even positive numbers can be misleading. If they are interpreted incorrectly, you run the risk of making unnecessary changes to the site, which will lead to lower conversions.

When we perform an A/B test, we create two competing web page versions. And we showed them to two groups of randomly selected people.

What is a statistical significance calculator in an A/B test, and why do we need to count it? Results are statistically significant if they are not due to random variation. In other words, you're unlikely to get two different conversion rates for page A and page B unless something specific changes. Statistical significance is a way to make sure your results are reliable before drawing any conclusions. And a calculator helps with this a lot.

You need to have confidence in the data before choosing the "winning" option. In statistical A/B testing, results are considered significant if they are not obtained by chance. Thus, achieving statistical significance with a confidence level of 95% means that the results will appear by chance only one time out of 20.

Here is an example of calculating statistical significance on a calculator. There are a lot of such calculators - you can choose the most convenient one for you. It's important to understand that whatever calculator you choose, it's important to interpret the results correctly.

Let's assume that after completing the A/B test of two web pages, we have the following data:

  • Page A: traffic 1200 visitors, conversion 2 visitors.
  • Page B: traffic 800 visitors, conversion 10 visitors.

We enter this data into the calculator to see if our results reached statistical significance. It is also important to choose a Confidence Level. This is the approximate value of statistical significance that we think our data has.

The result will be:

  • A conversion - 0,167%
  • B conversion - 1,25%
  • Uplift - 750%
  • P-value - 1,2%
  • Statistical value - 98,2%

Even an A/B test with the high statistical significance that the calculator showed can end up giving false positives. It's best to change things gradually. Optimizing one KPI (conversion) can negatively affect another, such as customer return. You need to keep an eye on all KPIs and avoid the classic importance mistakes.

Analyzing A/B Test Results with Plerdy

Plerdy streamlines the complex process of analyzing A/B test results. For businesses seeking actionable insights to optimize user experience, Plerdy is the go-to tool. Here’s how to dive deep into the results:

  • Gather Real-time Data: With Plerdy, stay on top of your A/B testing metrics. Instant data access ensures you're always clued into your test's progress.
  • Utilize the Calculator Feature: Crunch numbers with ease and precision. Whether you’re assessing conversion rates or user interactions, Plerdy's calculator helps you make sense of the stats.
  • Pinpoint Winning Variants: For an e-commerce niche, determine which product layout boosts sales. Or in content marketing, ascertain which headline reels in more readers.
  • Evaluate User Engagement: For a travel blog, measure which CTA garners more clicks for vacation packages. In tech startups, gauge which app interface increases user retention.
  • Adjust and Iterate: Based on your findings, tweak your strategies. If an educational portal sees higher enrollment with a particular banner design, it’s time to roll that out.

Harnessing Plerdy's robust functionalities lets businesses effectively decipher their A/B test results. By honing in on what truly resonates with users, it becomes simpler to optimize, adapt, and ultimately thrive in any niche. Dive into Plerdy and unlock unparalleled insights today.

What is a good A/B Testing Significance?

Understanding basic calculations will help you explain why your results are important to those unfamiliar with statistics.

The final value of statistical significance, which the calculator calculated in our example, is 98.8%. This is even higher than the initial 95%, so that we can change option B with some confidence.


A/B testing significance calculator is typically used by those who frequently interact with programs and websites. Webmasters, site administrators, SEO specialists, analysts, marketers, and UX researchers need it. It is important to note that you do not need special skills for testing. After testing, you can use a special calculator to determine A/B testing significance.

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