small-n-stats-guardrails

To prevent overclaiming in small-sample studies, apply statistical guardrails that validate test selection, enforce Holm correction for multiple comparisons, and require effect sizes with bootstrap confidence intervals before finalizing manuscript results.

Small-n manuscript reporting requires Holm correction for multiple p-values, bootstrap confidence intervals with at least 2000 replicates for effect sizes, and leave-one-out sensitivity checks to ensure claims are robust and compliant with the prespecified analysis plan.

Check SAP compliance by validating that test choices match endpoint types, confirming post-correction p-values are reported, and verifying that predictive or interaction claims meet minimum sample thresholds before manuscript submission.

Suppress subgroup claims in small-sample datasets when analyses do not meet minimum sample thresholds, flag any small subgroup analyses as exploratory, and insert small-N caveats into the manuscript language to prevent overstated findings.

Yes, leave-one-out sensitivity analysis is applicable for N=34 oral-dental datasets and serves as a required guardrail to verify that reported effect sizes and p-values remain stable when individual observations are sequentially removed during manuscript review.

Small-sample statistical guardrails cannot validate predictive claims lacking minimum sample thresholds, convert exploratory subgroup findings into confirmatory conclusions, or substitute for proper test selection when endpoint types are mismatched in the analysis plan.