Hypothesis Testing Expert

Guide practitioners through selecting, designing, and interpreting statistical hypothesis tests with proper assumptions checking, sample size calculations, and actionable conclusions.

Gold

v1.0.00 activationsData & AnalyticsTechnologyintermediate

SupaScore

84.4

Research Quality (15%)

8.6

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.4

Completeness (10%)

8.3

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.5

Best for

▸Selecting appropriate statistical test for comparing group means in A/B experiments
▸Validating normality and homoscedasticity assumptions before running ANOVA
▸Calculating minimum sample sizes for detecting meaningful effect sizes in user research
▸Interpreting p-values and confidence intervals without falling into statistical significance fallacies
▸Designing power analysis for clinical trial endpoint comparisons

What you'll get

●Structured test selection flowchart with assumption checks, Python/R code for validation, and interpretation guidelines
●Power analysis report with sample size recommendations, effect size justifications, and business impact context
●Complete hypothesis testing workflow from formulation through results interpretation with statistical and practical significance assessment

Not designed for ↓

×Machine learning model evaluation or predictive modeling tasks
×Causal inference from observational data without experimental design
×Time series analysis or forecasting statistical methods
×Bayesian statistical inference or posterior probability calculations

Expects

Clear research question, data types (continuous/categorical), sample characteristics, and practical significance thresholds for the business context.

Returns

Step-by-step statistical test selection, assumption validation code, sample size calculations, and interpretation framework with effect size context.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

hypothesis-testingstatisticsp-valuesignificance-testinganovat-testchi-squarepower-analysiseffect-sizeab-testingscipy

Research Foundation: 8 sources (3 books, 2 official docs, 2 academic, 1 industry frameworks)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Prerequisites

Use these skills first for best results.

Python Data AnalystGold

Works well with

A/B Test AnalystPlatinum Bayesian A/B Testing ExpertGold Experimental Design SpecialistGold Regression Analysis ExpertPlatinum Statistical Analysis AdvisorPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Causal Inference AnalystGold Bayesian Statistics ExpertPlatinum Monte Carlo Simulation ExpertGold

Common Workflows

Experiment Design to Analysis Pipeline

Complete experimental workflow from study design through statistical testing to business impact analysis

Experimental Design Specialist→hypothesis-testing-expert→A/B Test Analyst

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