[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CZ5SVY2BKMXH2BAT67AAWX":3,"skill-same:01M6CZ5SVY2BKMXH2BAT67AAWX":64},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":24,"linkOnly":25,"markdownContent":26,"markdownBody":27,"frontMatter":28,"refName":29,"commitSha":30,"skillDirectory":31,"forksCount":32,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":35,"install":36,"resources":49},"01M6CZ5SVY2BKMXH2BAT67AAWX","stat-compare-multiple-groups",null,"Compare a continuous variable across three or more groups using one-way ANOVA or Kruskal-Wallis with post-hoc testing.","Scientific-Tooling\u002Fstructured-intelligence","Scientific-Tooling","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F267453265?v=4","01M6CZ1SE6GGD4F67EAQ0PVX38","Scientific Tooling","01M6CZ1SJKCFVGH10Z3A152K02",4,"MIT","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstat-compare-multiple-groups\u002FSKILL.md",[],[19,20,21,22,23],"stat compare multiple groups","testing","three","across","ANOVA","2026-11-10 10:12:30",false,"---\nname: stat-compare-multiple-groups\ndescription: Compare a continuous variable across three or more groups using one-way ANOVA or Kruskal-Wallis with post-hoc testing.\n---\n\n# Skill: Multiple-Group Statistical Comparison\n\n## Use When\n\n- User wants to compare a continuous measurement across three or more experimental groups\n- User needs an omnibus test (ANOVA or Kruskal-Wallis) plus pairwise post-hoc comparisons\n- User has a two-factor design and wants to test for interaction effects (two-way ANOVA)\n\n## Inputs\n\n- Required:\n  - Data table (CSV or TSV) with a numeric value column and a group column (three or more levels)\n- Optional:\n  - Group variable column name\n  - Value variable column name\n  - Second factor column for two-way ANOVA (optional; triggers two-way analysis)\n  - Post-hoc method: `tukey` (default for ANOVA) or `dunn` (default for Kruskal-Wallis)\n  - Output directory (default: `.\u002Fmultigroup_output`)\n\n## Workflow\n\n1. Read data; validate that group column has three or more levels.\n2. Run normality tests (Shapiro-Wilk) within each group and run one global Levene's test across groups for homogeneity of variance.\n3. If all groups are normal and variances are approximately equal: one-way ANOVA.\n   - If second factor provided: two-way ANOVA with interaction term.\n4. If normality or homogeneity of variance is violated: Kruskal-Wallis test.\n5. If omnibus test is significant (p \u003C 0.05): run post-hoc pairwise comparisons.\n   - ANOVA: Tukey HSD (controls family-wise error rate).\n   - Kruskal-Wallis: Dunn test with Benjamini-Hochberg correction.\n6. Compute effect size: eta-squared (η²) for ANOVA, epsilon-squared for Kruskal-Wallis.\n7. Generate a box plot with all groups and pairwise significance brackets.\n8. Save the executable analysis script (`analysis_code.py` or `analysis_code.R`) and software version manifest (`session_info.txt`) used to produce results.\n9. Write results and plot (PDF) to output directory.\n10. Report: omnibus test result, effect size, significant pairwise comparisons.\n\n## Output Contract\n\n- Omnibus test result (TSV): test, statistic, df, p_value, effect_size, effect_size_type\n- Post-hoc comparisons table (TSV): group1, group2, statistic, p_value, adj_p_value, significant\n- Box plot with significance brackets (PDF)\n- Decision log explaining method selection (printed to stdout)\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- Two-way ANOVA assumes balanced or near-balanced design; unbalanced designs require Type III sums of squares.\n- Does not support repeated-measures ANOVA; use specialized tools for within-subject designs.\n- Tukey HSD assumes equal group sizes; Games-Howell is preferred for unequal variances.\n- Common failure: too few observations per group (n \u003C 5) makes tests unreliable.\n","# Skill: Multiple-Group Statistical Comparison\n\n## Use When\n\n- User wants to compare a continuous measurement across three or more experimental groups\n- User needs an omnibus test (ANOVA or Kruskal-Wallis) plus pairwise post-hoc comparisons\n- User has a two-factor design and wants to test for interaction effects (two-way ANOVA)\n\n## Inputs\n\n- Required:\n  - Data table (CSV or TSV) with a numeric value column and a group column (three or more levels)\n- Optional:\n  - Group variable column name\n  - Value variable column name\n  - Second factor column for two-way ANOVA (optional; triggers two-way analysis)\n  - Post-hoc method: `tukey` (default for ANOVA) or `dunn` (default for Kruskal-Wallis)\n  - Output directory (default: `.\u002Fmultigroup_output`)\n\n## Workflow\n\n1. Read data; validate that group column has three or more levels.\n2. Run normality tests (Shapiro-Wilk) within each group and run one global Levene's test across groups for homogeneity of variance.\n3. If all groups are normal and variances are approximately equal: one-way ANOVA.\n   - If second factor provided: two-way ANOVA with interaction term.\n4. If normality or homogeneity of variance is violated: Kruskal-Wallis test.\n5. If omnibus test is significant (p \u003C 0.05): run post-hoc pairwise comparisons.\n   - ANOVA: Tukey HSD (controls family-wise error rate).\n   - Kruskal-Wallis: Dunn test with Benjamini-Hochberg correction.\n6. Compute effect size: eta-squared (η²) for ANOVA, epsilon-squared for Kruskal-Wallis.\n7. Generate a box plot with all groups and pairwise significance brackets.\n8. Save the executable analysis script (`analysis_code.py` or `analysis_code.R`) and software version manifest (`session_info.txt`) used to produce results.\n9. Write results and plot (PDF) to output directory.\n10. Report: omnibus test result, effect size, significant pairwise comparisons.\n\n## Output Contract\n\n- Omnibus test result (TSV): test, statistic, df, p_value, effect_size, effect_size_type\n- Post-hoc comparisons table (TSV): group1, group2, statistic, p_value, adj_p_value, significant\n- Box plot with significance brackets (PDF)\n- Decision log explaining method selection (printed to stdout)\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- Two-way ANOVA assumes balanced or near-balanced design; unbalanced designs require Type III sums of squares.\n- Does not support repeated-measures ANOVA; use specialized tools for within-subject designs.\n- Tukey HSD assumes equal group sizes; Games-Howell is preferred for unequal variances.\n- Common failure: too few observations per group (n \u003C 5) makes tests unreliable.\n",{"name":5,"description":7},"main","5118e7fda890e9aa819cb71e003cd243d3a31970","skills\u002Fstat-compare-multiple-groups",1,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence","https:\u002F\u002Fgithub.com\u002FScientific-Tooling","2026-11-03 00:01:40",{"source":8,"skillName":5,"commands":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add Scientific-Tooling\u002Fstructured-intelligence --skill stat-compare-multiple-groups",{"tool":42,"command":43},"bunx","bunx skills add Scientific-Tooling\u002Fstructured-intelligence --skill stat-compare-multiple-groups",{"tool":45,"command":46},"pnpm","pnpm dlx skills add Scientific-Tooling\u002Fstructured-intelligence --skill 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or update repository skills that conform to local templates, provider metadata requirements, registry rules, and validation workflows.","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstructured-intelligence-skill-creator\u002FSKILL.md",[71],{"categoryNo":72,"categoryName":73,"taxonomyType":74,"taxonomyTypeDesc":75},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92],"structured intelligence skill creator","skill 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