Methodology
Every number QuantAI reports comes from a deterministic mathematical function — not an AI model. This page documents exactly which libraries, functions, and decision rules back each analysis, so you can cite your tools accurately and verify any result independently.
Computation Engine
SciPy ≥ 1.11Core statistical tests — t-tests, ANOVA, Mann-Whitney, Kruskal-Wallis, Wilcoxon, Pearson & Spearman correlation, chi-square, normality and homogeneity checks.
Virtanen et al. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17, 261–272.
statsmodels ≥ 0.14OLS and logistic regression, VIF computation, Durbin-Watson statistic, Breusch-Pagan homoscedasticity test, Tukey HSD post-hoc.
Seabold & Perktold (2010). Statsmodels: Econometric and statistical modeling with Python. Proceedings of the 9th Python in Science Conference.
pandas ≥ 2.0Data loading, missing data computation, listwise deletion, variable type detection, and Cronbach's alpha via covariance matrix.
McKinney (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference.
claude-sonnet-4-6Writes the APA Results paragraph only. Claude receives computed statistics and variable names — never raw data. It cannot modify or fabricate statistical values.
Anthropic (2024). Claude: A next-generation AI assistant. anthropic.com
12 Analyses
The exact function called for each analysis, the effect size reported, and any automatic corrections applied.
| Analysis | Library | Function / Method | Effect Size | Auto-correction |
|---|---|---|---|---|
| Independent Samples t-Test | scipy.stats | ttest_ind(equal_var=False if Levene p ≤ .05) | Cohen's d | Welch's applied automatically when Levene's p ≤ .05 |
| Paired Samples t-Test | scipy.stats | ttest_rel | Cohen's d | — |
| One-Way ANOVA | scipy.stats | f_oneway + Tukey HSD post-hoc | η² | — |
| Mann-Whitney U | scipy.stats | mannwhitneyu | rank-biserial r | — |
| Kruskal-Wallis | scipy.stats | kruskal | η² | — |
| Wilcoxon Signed-Rank | scipy.stats | wilcoxon | effect size r | — |
| Pearson Correlation | scipy.stats | pearsonr | r, 95% CI | — |
| Spearman Correlation | scipy.stats | spearmanr | ρ | — |
| OLS Linear Regression | statsmodels | OLS.fit() + VIF + Durbin-Watson + Breusch-Pagan | R², Adjusted R² | Heteroscedasticity flagged via Breusch-Pagan |
| Binary Logistic Regression | statsmodels | Logit.fit() | OR, 95% CI, McFadden R² | — |
| Chi-Square Test | scipy.stats | chi2_contingency | Cramér's V | Yates' correction for 2×2 tables |
| Cronbach's Alpha | numpy / pandas | Covariance matrix approach (k/(k-1)) × (1 − Σσᵢ²/σₜ²) | α, 95% CI | — |
Note. All functions are called with their default validated parameters unless a correction is explicitly triggered by an assumption check result.
Assumption Checking
QuantAI runs a suite of assumption checks before computing any statistic. Each check returns a green / yellow / red flag with a plain-language explanation and, where applicable, a recommendation. Flags are advisory — they do not block analysis, but they are reported alongside results so you can address them in your Methods section.
Method: pandas .isna() count across all analysis columns
Thresholds: 0% → green; <5% → yellow (listwise deletion noted); ≥5% → red (imputation recommended)
Method: Shapiro-Wilk (scipy.stats.shapiro) for n < 50; D'Agostino-Pearson (scipy.stats.normaltest) for n ≥ 50
Thresholds: p > .05 → assumption met (green); p ≤ .05 → assumption violated (red), non-parametric alternative recommended
Method: Levene's test (scipy.stats.levene)
Thresholds: p > .05 → equal variances (green); p ≤ .05 → unequal variances (yellow), Welch's correction applied automatically for t-tests
Method: 10:1 rule — minimum n = number of predictors × 10
Thresholds: Met → green; Not met → red with specific minimum reported
Method: statsmodels variance_inflation_factor per predictor
Thresholds: VIF < 5 → acceptable (green); 5–10 → moderate (yellow); > 10 → severe (red), removal/combination recommended
Method: Durbin-Watson statistic (statsmodels.stats.stattools.durbin_watson)
Thresholds: 1.5 < DW < 2.5 → errors independent (green); outside range → potential autocorrelation (yellow)
Method: Breusch-Pagan test (statsmodels.stats.diagnostic.het_breuschpagan)
Thresholds: p > .05 → homoscedastic (green); p ≤ .05 → heteroscedasticity detected (yellow), robust standard errors recommended
AI Narrative Generation
Claude receives
Claude never receives
Claude system prompt constraints (enforced on every call)
APA 7th Edition Formatting
Reference: American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
Verification
The verification section of the QuantAI homepage publishes reference datasets with known expected values — computed independently using SPSS and R — alongside the QuantAI output for the same data. You can download any reference dataset and run the same analysis in your preferred tool to confirm the values match.
Citing QuantAI in your Methods section
"Statistical analyses were conducted using QuantAI (quantai.study), which uses scipy.stats (Virtanen et al., 2020) and statsmodels (Seabold & Perktold, 2010) for statistical computation and Claude Sonnet (Anthropic, 2024) for APA 7th edition narrative generation. All analyses followed APA 7th edition reporting standards."
Data Sources
QuantAI analyzes any well-structured CSV, Excel, or SPSS file — survey exports, secondary databases (IPEDS, Census, administrative records), experimental data, or manually entered spreadsheets. Survey platform exports from Qualtrics, SurveyMonkey, Prolific, and Google Forms are cleaned automatically (extra header rows and metadata columns removed). For all other sources, upload your file as-is. The only requirement is one row per observation and numeric values in the columns you intend to analyze.