Purpose

This section documents the data preprocessing pipeline for 84 hypothesis tests undergoing multiple comparison corrections. Perfect data retention (100%) indicates no observations were excluded during cleaning, which is critical for maintaining the integrity of family-wise error rate (FWER) control calculations that depend on the complete test set.

Key Findings

Initial & Final Rows: 84 observations retained across both stages—no data loss occurred during preprocessing
Retention Rate: 100% indicates complete dataset integrity with zero exclusions
Rows Removed: 0 deletions suggests either pristine input data or that no quality thresholds triggered removal criteria
Train/Test Split: Not applicable, as this is a multiple comparison correction analysis rather than a predictive modeling task

Interpretation

The perfect retention rate ensures that all 84 tests remain eligible for correction methods (Holm, Bonferroni, BH, Hochberg). This is essential because FWER calculations depend on the complete family of tests; removing even one test would alter the stepdown thresholds and rejection decisions. The absence of preprocessing transformations preserves the raw p-values and effect sizes needed for accurate multiple comparison control.

Context

No train/test split is applicable here since the objective is statistical inference across a fixed test family, not predictive generalization. The analysis assumes all 84 tests