statistical-analysis-advisor

name	statistical-analysis-advisor
description	Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based on dataset characteristics, performs assumption checking, and provides power analysis guidance. Trigger when user asks about choosing statistical tests, checking statistical assumptions, or needs guidance on experimental design and sample size calculations.
version	1.0.0
category	Data
tags	[]
author	AIPOCH
license	MIT
status	Draft
risk_level	Medium
skill_type	Tool/Script
owner	AIPOCH
reviewer
last_updated	2026-02-06

Statistical Analysis Advisor

Intelligent statistical test recommendation engine that guides users through selecting the right statistical methods for their data.

Capabilities

1. Statistical Test Selection

   • Compares and recommends between T-test, ANOVA, Chi-square, Mann-Whitney, Kruskal-Wallis, etc.
   • Considers data type, distribution, sample size, and research question
   • Provides decision tree logic for test selection

2. Assumption Checking

   • Normality tests (Shapiro-Wilk, Kolmogorov-Smirnov)
   • Homogeneity of variance (Levene's test, Bartlett's test)
   • Independence verification
   • Outlier detection guidance

3. Power Analysis & Sample Size

   • Effect size estimation (Cohen's d, eta-squared, Cramér's V)
   • Sample size calculations for desired power
   • Post-hoc power analysis

Usage

from scripts.main import StatisticalAdvisor

advisor = StatisticalAdvisor()

# Get test recommendation
recommendation = advisor.recommend_test(
    data_type="continuous",
    groups=2,
    independent=True,
    distribution="normal"
)

# Check assumptions
assumptions = advisor.check_assumptions(
    data=[group1, group2],
    test_type="independent_ttest"
)

# Power analysis
power = advisor.calculate_power(
    effect_size=0.5,
    alpha=0.05,
    sample_size=30
)

Input Parameters

Parameter	Type	Description
data_type	str	"continuous", "categorical", "ordinal"
groups	int	Number of groups/comparison levels
independent	bool	Independent or paired/related samples
distribution	str	"normal", "non-normal", "unknown"