
Statistical Significance Calculator
from aaas-vault11
Guides data-analytics workflows to compute and interpret statistical significance, with step-by-step calculations, code snippets, and validation checks.
What it does
This skill helps agents calculate and interpret statistical significance for experiments and datasets. It provides step-by-step guidance for selecting appropriate tests (t-test, chi-square, ANOVA), computing p-values, confidence intervals, and power analyses, and generating production-ready code and configurations for common data stacks.
When to use it
Use this skill when you need to:
- Determine whether observed differences are statistically significant
- Choose and run the right hypothesis test for your data (continuous vs categorical)
- Generate reproducible code (Python/R/SQL) to compute p-values, effect sizes, and confidence intervals
- Validate analytics pipelines and check assumptions (normality, variance homogeneity, sample size/power)
What's included
- Scripts: none detected in the repo entry, but the skill provides example code patterns and validation steps in the instructions.
- References: none bundled, but the skill points to standard statistical best practices and industry guides.
- Instructions: clear procedural guidance for selecting tests, running calculations, and handling errors or missing data; example requests and expected outputs are included in the skill body.
Compatible agents
Best matched to data/analytics-oriented agent runtimes such as Claude Code, Cursor, and other LLMs that can generate runnable Python/R/SQL snippets.
Tags
Not yet audited
This skill has not been reviewed by our automated audit pipeline yet.







