Statistical Analysis Guide
Listing confounders and asking for assumption checks prevents the common mistake of running a test on data that violates its assumptions. The plain-English interpretation ensures you understand the results.
I need to analyze whether {{hypothesis}} using the following data: Dataset: {{dataset_description}} Variables: - Dependent variable: {{dependent_var}} ({{dv_type}}) - Independent variable(s): {{independent_vars}} - Potential confounders: {{confounders}} Sample size: {{sample_size}} Significance level: {{alpha}} Walk me through: 1. Which statistical test is appropriate and why (consider alternatives) 2. Assumptions to check before running the test, with code to check each one 3. The {{language}} code to run the analysis 4. How to interpret the output — what numbers matter and what they mean in plain English 5. How to report the results in a paper or presentation (APA format if applicable) 6. Limitations of this analysis and what could strengthen the conclusion
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Listing confounders and asking for assumption checks prevents the common mistake of running a test on data that violates its assumptions. The plain-English interpretation ensures you understand the results.
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