Statistical Significance Calculator
Determine if your results are statistically significant with t-tests, z-tests, and chi-square tests. Calculate p-values, confidence intervals, and get detailed statistical interpretations.
Select Test Type
One-Sample t-Test
Two-Sample t-Test
Z-Test
Chi-Square Test
One-Sample t-Test
Tests whether a sample mean differs significantly from a known population mean when the population standard deviation is unknown.
Significance Level (α)
0.05
0.01
0.10
Custom
Results
p = 0.000
Enter data to see results
How to Use the Statistical Significance Calculator
Statistical significance testing helps you determine whether observed differences in your data are likely due to real effects or just random chance. Our calculator supports multiple test types: One-Sample t-Test for comparing a sample mean to a known value, Two-Sample t-Test for comparing means between two groups, Z-Test for large samples with known population parameters, and Chi-Square Test for testing relationships between categorical variables. Simply select your test type, set your significance level (commonly 0.05), enter your data, and interpret the p-value results.
One-Sample t-Test Formula:
t = (x̄ - μ) / (s / √n)
p-value interpretation:
If p < α: Reject null hypothesis (statistically significant)
If p ≥ α: Fail to reject null hypothesis (not significant)
t = (x̄ - μ) / (s / √n)
p-value interpretation:
If p < α: Reject null hypothesis (statistically significant)
If p ≥ α: Fail to reject null hypothesis (not significant)
📊 Research Example:
Scenario: Testing if a new teaching method improves test scores
Null Hypothesis: New method has no effect (μ = 75)
Sample Data: n = 30 students, mean = 78.5, std dev = 8.2
Calculation: t = (78.5 - 75) / (8.2 / √30) = 2.34
Result: p = 0.026
Conclusion: Since p < 0.05, we reject the null hypothesis. The new teaching method significantly improves test scores.
Understanding statistical significance is crucial in research, quality control, A/B testing, and data-driven decision making. A p-value represents the probability of observing your results (or more extreme) if there were truly no effect. The significance level (α) is your threshold for making decisions - typically set at 0.05, meaning you're willing to accept a 5% chance of incorrectly concluding there's an effect when there isn't one. Remember that statistical significance doesn't necessarily mean practical significance, and always consider effect size and confidence intervals alongside p-values for comprehensive analysis.