Experimental Data Analysis

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.
• Inferential testing:
  • t-tests (independent/paired) and non-parametric alternatives when assumptions fail.
  • ANOVA (one-way and multi-way) with post-hoc testing (e.g., Tukey).
• Reporting outputs: test statistics, p-values, effect sizes, tables, charts, and explicit assumption notes.
• Reference materials for method selection and reporting templates:
  • references/stats-method-selection.md
  • references/reporting-template.md

Dependencies

• Python 3.10+
• pandas >= 2.0
• numpy >= 1.24
• scipy >= 1.10

Example Usage

The workflow is run-directory based. Initialize a new run, then analyze using the latest run by default.

# 1) Initialize a new run directory with sample inputs/config
python scripts/init_run.py

# 2) Run analysis (uses the latest outputs/runs/<timestamp>/ by default)
python scripts/analyze_experiment.py

Expected directory conventions:

• A new run directory is created at: outputs/runs/<timestamp>/
• Configuration file location: outputs/runs/<timestamp>/config.json
• All intermediate and final artifacts (config, inputs, outputs, figures, tables) must be written inside the run directory.
• Writing outside the run directory is prohibited.

Implementation Details

Reproducible Run Management

• Before each execution, run:
  • scripts/init_run.py to create outputs/runs/<timestamp>/ and populate initial inputs/config.
• Analysis scripts default to the latest run directory under outputs/runs/ unless explicitly overridden (if supported by the script).

Analysis Pipeline

1. Data Preparation

   • Handle missing values (e.g., drop, impute, or flag) according to the experimental design.
   • Detect and treat outliers (e.g., robust rules, domain thresholds), documenting any exclusions.
   • Identify variable roles:
     • Outcome variable(s): typically continuous measurements.
     • Grouping factors: categorical condition labels (treatment/control, timepoint, genotype, etc.).

2. Descriptive Statistics

   • Compute summary metrics per group:
     • Mean, standard deviation, and confidence intervals (commonly 95% CI).
   • Produce grouped summary tables suitable for reporting.

3. Inferential Statistics

   • Two-group comparisons
     • Use an independent t-test for separate groups.
     • Use a paired t-test for repeated measures / matched pairs.
     • If assumptions are violated, switch to an appropriate non-parametric alternative.
   • Multi-group / multi-factor comparisons
     • Use one-way ANOVA for a single factor with 3+ levels.
     • Use multi-way ANOVA when multiple factors are present.
   • Multiple comparisons
     • Apply post-hoc procedures (e.g., Tukey) after ANOVA when needed.
     • Define and document the multiple-comparison control strategy.

4. Assumption Checks and Reporting Standards

   • Validate and report:
     • Normality (per group or model residuals, as appropriate).
     • Homogeneity of variance.
   • Report, at minimum:
     • Test statistic, degrees of freedom (if applicable), p-value.
     • Effect size(s) and confidence intervals where applicable.
   • Retain analysis code and random seeds to ensure reproducibility.