statistical-method-selection

Research

Select appropriate statistical methods for experiment analysis

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Tactic: Statistical Method Selection

Orchestration Pattern

Assess Data Characteristics → Determine distribution type, sample size, pairing structure
metric-specification → Ensure metrics are well-defined and measurable
Select Test Family → Choose between parametric, non-parametric, or Bayesian
sample-size-estimation → Power analysis for the selected test
Define Analysis Pipeline → Pre-register the complete analysis plan

Decision Criteria

Condition	Recommended Method
Normal data, 2 groups, paired	Paired t-test
Normal data, 2 groups, unpaired	Welch's t-test
Normal data, 3+ groups	ANOVA + post-hoc (Tukey HSD)
Non-normal, 2 groups	Wilcoxon signed-rank / Mann-Whitney U
Non-normal, 3+ groups	Kruskal-Wallis + Dunn's test
Multiple datasets, multiple methods	Friedman + Nemenyi / critical difference
Want probability of superiority	Bayesian comparison (Benavoli 2017)
Small sample, no distributional assumptions	Permutation test
Variance estimation needed	Bootstrap confidence intervals
Multiple comparisons	Apply Holm-Bonferroni correction

Quality Checks

Is the test appropriate for the data type (continuous, ordinal, categorical)?
Are independence assumptions met? (If not, use paired/repeated-measures variants)
Is the sample size sufficient for the chosen test's power requirements?
Are multiple comparison corrections applied when testing multiple hypotheses?
Is the effect size reported alongside p-values?
Is the significance threshold pre-registered (not chosen post-hoc)?

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
metric-specification	Define experiment metrics and significance standards
sample-size-estimation	SOP: power analysis and required experiment count estimation