A/B Test Statistical Significance Calculator
What is A/B Testing Statistical Significance?
A/B testing, also known as split testing, is a method of comparing two versions of a webpage, email, or other marketing asset to determine which one performs better. Statistical significance is a mathematical way of determining whether the difference in performance between your two versions is meaningful or if it was likely just due to random chance.
How to Use This Calculator
To check the significance of your A/B test, you need four numbers:
- Visitors (Version A): The total number of users who saw your original version (the "control").
- Conversions (Version A): The number of users who took the desired action (e.g., clicked a button, signed up) on the control version.
- Visitors (Version B): The total number of users who saw your new version (the "variation").
- Conversions (Version B): The number of users who converted on the variation.
Enter these values and click the button to see the results. The calculator will tell you if the change had a statistically significant impact.
Understanding the Results
This calculator analyzes your data to provide a clear conclusion. Here is what the key terms in the results mean:
| Term | Description |
|---|---|
| Conversion Rate | The percentage of visitors who completed the desired action. It is calculated as (Conversions / Visitors) × 100. |
| Uplift or Change | The percentage increase (uplift) or decrease in the conversion rate of the variation compared to the control. |
| Statistical Significance (Confidence Level) | The probability that the observed difference between your two versions is a real effect and not due to random chance. In digital marketing, a confidence level of 95% or higher is the standard threshold to declare a result significant. |
Two-Tailed vs. One-Tailed Tests
This calculator performs a two-tailed test, which is generally preferred for A/B testing. Here is the difference:
- A one-tailed test only checks if Version B is significantly better than Version A. It cannot determine if a negative result is statistically significant.
- A two-tailed test checks for any significant difference, whether it is positive or negative. This is more robust because it can tell you with confidence if your change had a real positive impact, a real negative impact, or no discernible impact at all.
Why Is Statistical Significance Important?
Without checking for statistical significance, you might make important business decisions based on flawed data. An A/B test statistical significance calculator helps you wait until you have enough data to be confident that your results are real and repeatable, preventing you from acting on a false positive or incorrectly dismissing a change that had a real negative impact.