This is a PhD-level statistical analysis skill that walks you through the mechanics of choosing and running the right test for your data. It checks assumptions like normality and homoscedasticity, calculates effect sizes and confidence intervals, and keeps you from falling into p-hacking traps. The output format is structured around test rationale, numerical results, and practical significance rather than just binary significance. What's refreshing is the checkpoint at the end that asks about sensitivity analysis and non-parametric alternatives, which catches the stuff you might forget when you're deep in the weeds. Use it when you need statistical rigor and want to avoid the common mistakes that make reviewers roll their eyes.
npx -y skills add poemswe/co-researcher --skill quantitative-analysis --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
juliusbrussee/caveman
mattpocock/skills
shadcn/improve
obra/superpowers
forrestchang/andrej-karpathy-skills
vercel-labs/skills