Statistical Significance Calculator
A/B Testing Tools
Are you wondering if a design or copy change impacted your email or landing page's conversion rate? Use this A/B testing tool to calculate the statistical significance of your A/B test to make truly data-driven decisions.
Statistical Significance Calculator
| Samples | Conversions | CR% | ||
|---|---|---|---|---|
 |
A (Control) | |||
|
B (Variant) |
| 99-100% | Results are highly significant (this is a sure thing). |
| 95-98% | Results are statistically significant (good enough for academic publishing). |
| 90-94% | Results tend toward statistical significance (good for a rough sense). |
| 51-89% | Results are not statistically significant (could just be a fluke). |
| <= 50% | Results are not statistically significant (likely a fluke). |
* Results assume experiment was set up the correctly.
** Results calculated using the chi-squared statistical hypothesis test.
How to do A/B Testing
Read this guide for an A/B testing framework that will set you up for digital marketing success and learn how to use a/b testing statistics to deliver more personalized customer experiences, and drive more revenue while growing your business.
Download This eBook to Learn:
- What is A/B testing?
- The difference between A/B Testing vs. Multivariate Testing
- Tips for getting the most out of your A/B testing methods
- Best practices and innovative techniques for A/B testing
- Real-world examples of A/B testing success
- Example #1: Highly significant; sample sizes are not evenly split between test groups; same number of conversions; higher conversion rate.
- Example #2: Not statistically significant; One group has more conversions but a lower conversion rate.
- Example #3: Not statistically significant; Even sample distribution; higher number of conversion and conversion rate.
- Example #4: Statistically significant; Even sample distribution; higher number of conversion and conversion rate.