domains / families / science / experimental-data-analysis

experimental-data-analysis

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Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.

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.

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