How do I use the "Calculate Statistical Significance" prompt?

Copy the prompt template, fill in the variables (TEST_TYPE, SAMPLE_DATA, SIGNIFICANCE_LEVEL, HYPOTHESIS), and paste it into ChatGPT, Claude, or Gemini.

What AI tools work with this data prompt?

This prompt works with chatgpt, claude, gemini. Simply copy and paste the template into any of these AI assistants.

Tips for getting the best results?

Always specify your hypothesis clearly before running the test to avoid p-hacking Include effect size calculations alongside p-values for practical significance assessment Check statistical assumptions before interpreting results - violations can invalidate conclusions Provide both the raw data and summary statistics for more accurate calculations Consider the business context when interpreting statistical significance vs practical significance

data intermediate

Calculate Statistical Significance

Get accurate statistical significance calculations with confidence intervals and p-values. Perfect for A/B tests and research analysis.

Works with: chatgptclaudegemini

Prompt Template

I need you to calculate statistical significance for my data analysis. Please act as a statistician and perform a comprehensive statistical significance test. Here are the details: - Test Type: [TEST_TYPE] - Sample Data: [SAMPLE_DATA] - Significance Level: [SIGNIFICANCE_LEVEL] - Hypothesis: [HYPOTHESIS] Please provide: 1. **Test Selection Justification**: Explain why the chosen test is appropriate for this data 2. **Assumptions Check**: Verify if the data meets the test requirements (normality, independence, etc.) 3. **Calculations**: Show step-by-step calculations including: - Test statistic value - Degrees of freedom (if applicable) - P-value - Critical value(s) 4. **Results Interpretation**: - Whether to reject or fail to reject the null hypothesis - Confidence interval (if applicable) - Effect size measurement - Practical significance assessment 5. **Conclusion**: Provide a clear, non-technical summary of what the results mean in practical terms 6. **Recommendations**: Suggest next steps based on the findings If any assumptions are violated, please suggest alternative tests or data transformations. Format your response clearly with headers and highlight key findings.

Variables to Customize

[TEST_TYPE]

The type of statistical test to perform

Example: Two-sample t-test comparing conversion rates between control and treatment groups

[SAMPLE_DATA]

The actual data or summary statistics to analyze

Example: Control group: n=500, conversions=45 (9%); Treatment group: n=520, conversions=67 (12.9%)

[SIGNIFICANCE_LEVEL]

The alpha level for the test

Example: 0.05

[HYPOTHESIS]

The null and alternative hypotheses being tested

Example: H0: There is no difference in conversion rates between groups. H1: Treatment group has higher conversion rate than control group

Example Output

## Statistical Significance Analysis ### Test Selection Justification A two-proportion z-test is appropriate here since we're comparing conversion rates between two independent groups with large sample sizes (n>30). ### Assumptions Check ✓ Independence: Groups are independent ✓ Sample size: Both groups have n>30 and np>5, n(1-p)>5 ✓ Random sampling: Assumed from experimental design ### Calculations - **Pooled proportion**: p̂ = (45+67)/(500+520) = 0.1098 - **Standard error**: SE = √[0.1098(1-0.1098)(1/500+1/520)] = 0.0196 - **Test statistic**: z = (0.129-0.09)/0.0196 = 1.99 - **P-value**: 0.0233 (one-tailed) - **Critical value**: 1.645 at α=0.05 ### Results Interpretation **Decision**: Reject the null hypothesis (z = 1.99 > 1.645, p = 0.0233 < 0.05) **95% Confidence Interval**: (0.005, 0.077) for the difference in proportions **Effect Size**: Cohen's h = 0.125 (small effect) ### Conclusion The treatment group shows a statistically significant improvement in conversion rate (12.9% vs 9.0%, p = 0.023). The difference of 3.9 percentage points is unlikely due to chance. ### Recommendations 1. Implement the treatment as it shows significant improvement 2. Monitor long-term performance to ensure sustained effect 3. Consider testing variations to optimize further

Pro Tips for Best Results

Always specify your hypothesis clearly before running the test to avoid p-hacking
Include effect size calculations alongside p-values for practical significance assessment
Check statistical assumptions before interpreting results - violations can invalidate conclusions
Provide both the raw data and summary statistics for more accurate calculations
Consider the business context when interpreting statistical significance vs practical significance