A/B Test Significance Calculator

What Is Statistical Significance?

According to the Harvard Business Review in an interview with Thomas Redman, statistical significance helps determine whether an observed result is due to chance or a real influencing factor. If you make a change in your online shop—such as testing an alternative product layout—you’ll want to know if the higher conversion rate in the test variant is just a fluke. Without statistical significance, results can lead to incorrect conclusions, resulting in inefficient or even counterproductive decisions. If you want your tests to be valid and reproducible, checking for statistical significance is essential.

What Should You Consider When It Comes to Statistical Significance?

• Sample size: The size of your sample greatly affects the reliability of your results. A small sample size may lead to random variations, whereas larger samples reduce this risk. The larger the sample, the more accurately you can assess the differences between variants. However, keep in mind that larger sample sizes may require more time and resources. It’s important to find a reasonable balance between precision and effort.
• Variation within the population: If the data within your audience varies significantly (e.g., very different purchase amounts), there’s a higher risk that the sample won’t be representative. When variation is low, results tend to be more reliable because the sample reflects the population more accurately. That’s why it’s important to assess variability before running your analysis.
• Significance level: The significance level indicates how confident you can be in your test results. It shows the probability that an observed difference (e.g., between two A/B test variants) occurred purely by chance. A common threshold is 5% (p < 0.05). That means there’s only a 5% chance the result is random, and a 95% chance it reflects a real effect. Why does this matter? The significance level helps you trust your results. It tells you: “If you repeat the test, there’s a 95% chance you’ll see a similar outcome.” This allows you to make informed decisions while minimizing the risk of error.

Statistical significance helps determine whether differences between variants are real or due to chance. Factors like sample size, population variation, and the chosen significance level influence how reliable your results are. A 5% significance level gives you 95% confidence in the validity of your findings. Use our A/B Test Significance Calculator to quickly and easily determine whether your test results are statistically significant.