Statistical Significance Calculator

Quickly determine if your A/B test or experiment results are statistically significant. What does this mean? Statistical significance shows whether your results are due to real effects or just chance. It's key for data-driven decisions.

Use this tool to confidently validate the results of A/B tests, feature experiments, or marketing campaigns. It's designed for product managers, UX researchers, and marketers.

When to Use the Calculator

Product Managers

Use this calculator when validating the impact of new features or changes in your product. For example:

  • Testing a new onboarding flow.
  • Measuring the success of a pricing change.
  • Assessing feature adoption rates.

UX Researchers

Use it to measure the effectiveness of design changes or usability tests. For example:

  • Comparing two versions of a landing page design.
  • Evaluating the success of a new navigation layout.
  • Validating user satisfaction metrics.

Marketers

Ensure your campaigns deliver measurable results. For example:

  • Testing two email subject lines in an A/B test.
  • Comparing ad performance across demographics.
  • Validating click-through or conversion rates for campaigns.

Enter Your Data

Result:

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P-value: {{ pValue }}

Z-Score: {{ zScore }}