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name: auto-hypothesis-test description: "Automatically selects and runs the right statistical test for your data — t-test, ANOVA, chi-square, Mann-Whitney, or others — and provides plain-language interpretations of the results. Triggered when you ask about group comparisons, significance, p-values, hypothesis testing, or mention specific tests like t-test, ANOVA, or chi-square." license: MIT

auto-hypothesis-test

Automated statistical testing tool — automatically selects the appropriate hypothesis test based on your data characteristics (t-test / chi-square / ANOVA / Mann-Whitney, etc.) and outputs results with plain-language interpretations.

Capabilities

Feature	Description
Independent samples t-test	2 groups + normal data, compare means
Welch's t-test	2 groups + normal but unequal variances
Mann-Whitney U	2 groups + non-normal data (nonparametric)
One-way ANOVA	3+ groups + normal data
Kruskal-Wallis	3+ groups + non-normal data (nonparametric)
Chi-square independence test	Association between two categorical variables
Paired t-test	Before/after comparison (normal)
Wilcoxon signed-rank	Before/after comparison (nonparametric)
Auto-selection	Automatically chooses based on group count, normality, and data type
Plain-language interpretation	Every metric and conclusion explained in everyday language

Quick Start

# Group comparison (auto-selects the test)
python3 scripts/statistical_test_suite.py data.csv --group treatment --value score

# Chi-square test (two categorical variables)
python3 scripts/statistical_test_suite.py survey.csv --group gender --value preference

# Paired test (before/after comparison)
python3 scripts/statistical_test_suite.py experiment.csv --col1 pre_score --col2 post_score --paired

# Force a specific test
python3 scripts/statistical_test_suite.py data.csv --group group --value score --test mann-whitney

# Save results to JSON
python3 scripts/statistical_test_suite.py data.csv -g treatment -v score -o result.json

Detailed Usage

Mode 1: Group Comparison

Use --group to specify the grouping column and --value to specify the comparison column. The tool automatically determines which test to use.

python3 scripts/statistical_test_suite.py <data-file> --group <group-col> --value <value-col> [options]

Auto-selection logic:

1. Both columns are categorical → Chi-square test
2. 2 groups + data is normal → Independent samples t-test (Welch's t if variances are unequal)
3. 2 groups + data is non-normal → Mann-Whitney U test
4. 3+ groups + data is normal → One-way ANOVA
5. 3+ groups + data is non-normal → Kruskal-Wallis test

Mode 2: Paired Comparison

Use --col1 and --col2 to specify the two measurement columns.

python3 scripts/statistical_test_suite.py <data-file> --col1 <before> --col2 <after> --paired [options]

Auto-selection logic:

1. Differences are normal → Paired t-test
2. Differences are non-normal → Wilcoxon signed-rank test

Parameters

Parameter	Short	Required	Default	Description
input	—	Yes	—	Input file path (CSV/TSV/Excel/JSON)
--group	-g	Mode 1	—	Grouping variable column name
--value	-v	Mode 1	—	Numeric/categorical variable column name
--col1	—	Mode 2	—	First variable column for paired test
--col2	—	Mode 2	—	Second variable column for paired test
--paired	—	No	false	Enable paired test mode
--test	-T	No	Auto	Force a specific test (see list below)
--alpha	-a	No	0.05	Significance level
--output	-o	No	stdout	Path to save result JSON

Available Tests (--test)

t-test / mann-whitney / anova / kruskal-wallis / chi-square / paired-ttest / wilcoxon

Output Structure (JSON)

{
  "test": "Independent samples t-test",
  "test_id": "independent_ttest",
  "statistic": 2.3456,
  "p_value": 0.0213,
  "effect_size": {"cohens_d": 0.4821},
  "group_stats": {
    "Control": {"n": 30, "mean": 72.5, "std": 8.3},
    "Treatment": {"n": 30, "mean": 78.1, "std": 7.9}
  },
  "normality_check": {"Control": "Shapiro-Wilk W = 0.97, p = 0.52 (normal)", "...": "..."},
  "selection_reason": ["2 groups + approximately normal data → selected independent samples t-test"],
  "alpha": 0.05,
  "interpretation": [
    "Test method: Independent samples t-test",
    "Significance level: α = 0.05",
    "Conclusion: p = 0.0213 < 0.05, the difference is statistically significant.",
    "Effect size: Cohen's d = 0.4821 (medium effect, notable difference)",
    "Plain-language summary: There is a significant difference between 'Control' (mean 72.5) and 'Treatment' (mean 78.1)…"
  ]
}

Dependencies

• Python 3.8+
• pandas
• numpy
• scipy

pip install pandas numpy scipy