Statistical Significance Calculator
Two-proportion z-test: is the difference between two percentages real or noise?
Free · No signup · By the analysts at Blackridge Research · Updated 2026-07-18
Inputs
Results
What this means
Adjust the inputs to calculate.
About the Statistical Significance Calculator
Statistical significance tells you whether the difference between two proportions is real or just random noise.
The two-proportion z-test is the standard method for A/B testing and survey comparison.
Formula
z = (p₁ - p₂) / √(p̂(1-p̂)(1/n₁ + 1/n₂))
- z
- — Z-Score
- p₁
- — Proportion 1
- p₂
- — Proportion 2
- n₁
- — Sample Size 1
- n₂
- — Sample Size 2
How to use this calculator
- 1
Enter proportion 1
The first percentage (e.g., conversion rate).
- 2
Enter sample size 1
Sample size for proportion 1.
- 3
Enter proportion 2
The second percentage to compare.
- 4
Enter sample size 2
Sample size for proportion 2.
Example calculations
A/B test analysis
A/B test: Version A has 65% conversion (n=500), Version B has 55% conversion (n=500).
z = 3.25, p = 0.001. The difference is statistically significant.
- Z-Score:
- 3.25
- P-Value:
- 0.001
- Statistically Significant?:
- Yes (p < 0.05)
- Difference:
- 10.0%
Z = 3.25, p = 0.001. Statistically significant difference of 10 percentage points.
Interpreting your results
p < 0.05 means the difference is statistically significant — it's unlikely to be due to chance.
p < 0.01 means the difference is highly significant.
p > 0.05 means the difference is not statistically significant — it could be due to chance.
Need the market data behind this calculator?
The Statistical Significance Calculator is only as good as its inputs. Blackridge Research publishes syndicated market reports with vetted market sizes, growth rates, and competitive landscapes across 40+ industries — and builds custom studies when the shelf report doesn't exist.
Industry applications
Business
- A/B testing: determine if test results are statistically significant.
- Marketing experiments: validate campaign performance differences.
- Product testing: compare user satisfaction between versions.
Construction
- Quality testing: validate quality differences between suppliers.
- Method comparison: compare construction methods for significance.
Research
- Survey analysis: determine if demographic differences are significant.
- Market studies: test differences between segments.
Common mistakes to avoid
-
✗ Confusing statistical significance with practical significance
A 0.5% difference can be statistically significant with large samples but practically irrelevant.
-
✗ Failing to state the significance level
Always state the significance level (e.g., α = 0.05).
Frequently asked questions
›What is statistical significance?
The probability that an observed difference is not due to chance.
›What p-value indicates significance?
p < 0.05 indicates significance at the 95% confidence level.
Glossary
- Z-test:
- A statistical test for comparing two proportions.
- P-value:
- The probability of observing the data if the null hypothesis is true.
- Statistical significance:
- The conclusion that an effect is unlikely to be due to chance.
- Null hypothesis:
- The assumption that there is no real difference.
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