Hypothesis Testing

关于

Hypothesis Testing provides a comprehensive framework for navigating the complexities of statistical analysis within research workflows. It assists users in selecting the most appropriate tests—ranging from t-tests and ANOVA to Chi-square and non-parametric alternatives—based on variable types and study design. The skill ensures scientific rigor by providing structured guidance on validating assumptions like normality and homogeneity of variance, offering clear paths for troubleshooting violations, and establishing standardized patterns for reporting results with effect sizes and confidence intervals.

主要功能

• Guidelines for interpreting p-values, effect sizes, and confidence intervals
• Standardized reporting templates for academic and professional findings
• Decision tree for selecting tests based on variable types and counts
• 2 GitHub stars
• Reference library for common statistical tests and their requirements
• Detailed assumption checking for parametric and non-parametric tests

使用场景

• Formatting statistical results according to standard research reporting practices
• Choosing the correct statistical test for a specific experimental design
• Validating data assumptions and selecting non-parametric alternatives when necessary