Significance Calculator
Stop guessing your A/B test results. Calculate p-values and confidence intervals to make data-driven decisions for your business.
A/B Parameters
Visitors
Conversions
Visitors
Conversions
Industry Standard
Most marketers and scientists aim for a **95% Confidence Level** before declaring a result as "statistically significant".
Not Significant Yet
Confidence level is only **94.0%**. You may need more data or the variation has no real impact.
Improvement
+40.0%
Lift in conversion rate
P-Value
0.0597
Probability of null hypothesis
Conversion Performance
Understanding Significance
Statistical significance helps you decide if the difference between your control and variation is real or just a result of random fluctuations in the data.
The P-Value Explained
A p-value of 0.05 means there is only a 5% chance the observed difference happened by accident.
- P < 0.05: Statistically Significant (Winner)
- P > 0.05: Not Significant (Inconclusive)
- Confidence = (1 - P) * 100
Key Components
Control Group
The baseline or original version of your experiment.
Variation Group
The new version you are testing against the control.
Confidence Level
The probability that the test results are accurate and repeatable.
Sample Size
The number of visitors needed to reach a conclusive result.
CRO Tip
"Don't stop a test too early just because you see a winner. Wait until you have enough sample size and significance to avoid the 'Peeking Problem'."
Common Questions
What is a good confidence level?
Most experts use 95% as the gold standard. For high-stakes decisions, 99% is preferred.
What if the result is negative?
A negative significance means your variation performed worse than the control. This is still a valuable learning!
How long should I run a test?
At least one full business cycle (usually 7 days) to account for day-of-the-week variations.