serejaris 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:
- Both columns are categorical → Chi-square test
- 2 groups + data is normal → Independent samples t-test (Welch's t if variances are unequal)
- 2 groups + data is non-normal → Mann-Whitney U test
- 3+ groups + data is normal → One-way ANOVA
- 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:
- Differences are normal → Paired t-test
- 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