StatPilot
Automated, Transparent Statistical Analysis & Decision Engine
StatPilot is an automated statistical decision engine engineered in Python for researchers, clinical trialists, and data scientists. StatPilot evaluates distribution normality (Shapiro-Wilk, D’Agostino-Pearson) and variance homogeneity (Levene’s test) across experimental datasets to select and execute the mathematically optimal parametric or non-parametric statistical tests, outputting audit-ready statistical reports.
Core Python Library Stack
Python 3.9+
Core Engine
Pandas
Dataframe Wrangling
SciPy
Hypothesis Testing
Rich CLI
Formatted Terminal Reports
Seaborn
Diagnostic Plots
PyTest
100% Test Coverage
CLI & API
Dual Invocation
Key Engineering Capabilities
Automated Assumption Testing
Evaluates Shapiro-Wilk normality and Levene’s variance homogeneity before running hypothesis tests to eliminate statistical bias.
Parametric vs Non-Parametric Decision Tree
Dynamically selects between Student's t-test, Mann-Whitney U, ANOVA, or Kruskal-Wallis based on empirical distribution properties.
Audit-Ready Markdown & Rich Terminal Outputs
Outputs human-readable and publication-ready statistical summary tables with exact p-values, test statistics, and effect sizes.
CLI & Python Library Invocation
Callable via simple terminal commands (`statpilot data.csv`) or directly imported into Jupyter notebooks and automated data pipelines.
Built For Data Leaders
Clinical Trial Statisticians
Eliminate manual testing selection errors when evaluating clinical biomarker group differences.
Data Science Engineering Teams
Integrate automated statistical validation checks into automated ML feature engineering pipelines.
Academic & Technical Authors
Generate transparent, reproducible statistical method documentation for peer-reviewed submissions.
Related Platforms
Interested in integrating StatPilot into your analytics infrastructure?
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