Individual skill
experimental-data-analysis
Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.
scientific-skills/Data Analysis/experimental-data-analysis/SKILL.md
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- bioinformatics Repository topic
- computational-biology Repository topic
- data-analysis Repository topic
- ai Repository topic
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- cancer-genomics Repository topic
- data-visualization Repository topic
- differential-expression Repository topic
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- testing Derived category
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SKILL.mdcore · 4,502 bytesSKILL.mdcore · 4,502 bytesexperimental-analysis_audit_result_v1.jsonasset · 12,269 bytesreferences/reporting-template.mddoc · 217 bytesreferences/stats-method-selection.mddoc · 382 bytesscripts/analyze_experiment.pyruns · 6,283 bytesscripts/init_run.pyruns · 1,683 bytes
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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
## When to Use
- You have experimental results in CSV form and need a reproducible end-to-end analysis workflow (clean → test → report).
- You need to compare two conditions (independent or paired) and determine statistical significance with effect sizes.
- You need to compare 3+ groups (one-way) or multiple factors (multi-way) using ANOVA and post-hoc multiple comparisons.
- You must validate assumptions (normality, homogeneity of variance) and document them in a report.
- You need standardized run outputs (timestamped run directories) for traceability and auditing.
## Key Features
- Reproducible, run-based execution that writes all artifacts into `outputs/runs/<timestamp>/`.
- Data preparation guidance: missing values, outliers, and variable type identification (continuous/categorical; grouping factors).
- Descriptive statistics: means, standard deviations, confidence intervals, and grouped summary tables.