https://github.com/ywatanabe1989/scitex-stats

Publication-ready statistical testing with 23 tests, effect sizes, power analysis, and APA formatting
https://github.com/ywatanabe1989/scitex-stats

apa effect-size hypothesis-testing mcp-server power-analysis python scitex statistical-testing statistics

Last synced: 4 months ago
JSON representation

Publication-ready statistical testing with 23 tests, effect sizes, power analysis, and APA formatting

Host: GitHub
URL: https://github.com/ywatanabe1989/scitex-stats
Owner: ywatanabe1989
License: agpl-3.0
Created: 2026-03-10T19:40:21.000Z (4 months ago)
Default Branch: main
Last Pushed: 2026-03-26T01:18:51.000Z (4 months ago)
Last Synced: 2026-03-26T05:07:07.438Z (4 months ago)
Topics: apa, effect-size, hypothesis-testing, mcp-server, power-analysis, python, scitex, statistical-testing, statistics
Language: Python
Homepage: https://scitex.ai
Size: 1.01 MB
Stars: 0
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

          # SciTeX Stats (scitex-stats)



  

    

  



Publication-ready statistical testing with 23 tests, effect sizes, power analysis, and APA formatting




  

  

  

  





  Full Documentation · pip install scitex-stats



---

## Problem

Statistical testing in Python is fragmented across `scipy`, `statsmodels`, and `pingouin` — each with different interfaces and output conventions. Getting publication-ready results requires substantial manual work: computing effect sizes, running power analysis, formatting to APA or journal standards. AI agents face a further barrier: they cannot call Python libraries directly and need structured, tool-based access.

## Solution

scitex-stats provides a unified interface that covers the full statistical workflow:

- **23 statistical tests** with automatic recommendation based on data characteristics

- **Built-in effect sizes** (Cohen's d, Cliff's delta, eta squared), **power analysis**, and **APA-formatted output**

- **Three interfaces** — Python API, CLI, and MCP server — so human researchers and AI agents use the same engine

```mermaid

flowchart LR

    A[Raw Data] --> B{Recommend Test}

    B --> C[Run Test]

    C --> D[Effect Size]

    C --> E[Power Analysis]

    D --> F[APA Format]

    E --> F

    F --> G[Publication-Ready Result]

    style A fill:#4a90d9,stroke:#2c3e50,color:#fff

    style B fill:#f5a623,stroke:#2c3e50,color:#fff

    style C fill:#27ae60,stroke:#2c3e50,color:#fff

    style D fill:#8e44ad,stroke:#2c3e50,color:#fff

    style E fill:#8e44ad,stroke:#2c3e50,color:#fff

    style F fill:#e74c3c,stroke:#2c3e50,color:#fff

    style G fill:#2c3e50,stroke:#1a252f,color:#fff

```

*Figure 1. Statistical testing workflow. scitex-stats automates the full pipeline from raw data to publication-ready results: test recommendation based on data characteristics, test execution with effect size and power analysis, and APA-formatted output.*

Every test returns a **unified result dictionary** with consistent keys:

```json

{

  "test_method": "Student's t-test (independent)",

  "statistic": -3.210,

  "stat_symbol": "t",

  "alternative": "two-sided",

  "n_x": 30,

  "n_y": 30,

  "pvalue": 0.0022,

  "stars": "**",

  "alpha": 0.05,

  "significant": true,

  "effect_size": -0.829,

  "effect_size_metric": "Cohen's d",

  "effect_size_interpretation": "large",

  "power": 0.884,

  "H0": "μ(x) = μ(y)",

  "formatted": "t = -3.210, p = 0.0022, Cohen's d = -0.829, **"

}

```

*Table 3. Unified result format. All 23 tests return the same dictionary structure with test statistics, p-value, effect size with interpretation, statistical power, and APA-formatted string.*

## Installation

Requires Python >= 3.10.

```bash

pip install scitex-stats

# With MCP server for AI agents

pip install scitex-stats[mcp]

# Everything

pip install scitex-stats[all]

```

> **SciTeX users**: `pip install scitex` already includes Stats. Use `import scitex` then `scitex.stats`.

## Quickstart

```python

import scitex_stats as ss

# Get test recommendation

ctx = ss.StatContext(n_groups=2, sample_sizes=[30, 30], outcome_type="continuous", design="between", paired=False)

recs = ss.recommend_tests(ctx)

# Run a test

result = ss.run_test("ttest_ind", data=group1, data2=group2)

# APA-formatted output

print(result["formatted"])

```

## Three Interfaces

Python API




```python

import scitex_stats as ss

# Automatic test recommendation

ctx = ss.StatContext(n_groups=2, sample_sizes=[30, 30], outcome_type="continuous", design="between", paired=False)

recs = ss.recommend_tests(ctx)

# Run a test

result = ss.run_test("ttest_ind", data=group1, data2=group2)

# Effect sizes

from scitex_stats import effect_sizes

d = effect_sizes.cohens_d(group1, group2)

# Power analysis

from scitex_stats import power

n = power.sample_size_ttest(effect_size=0.5, alpha=0.05, power=0.8)

# Multiple comparison correction

from scitex_stats import correct

corrected = correct.correct_fdr(results)

# Post-hoc tests

from scitex_stats import posthoc

results = posthoc.posthoc_tukey(groups)

```

> **[Full API reference](https://scitex-stats.readthedocs.io/)**

CLI Commands




```bash

scitex-stats --help-recursive                # Show all commands

scitex-stats list-python-apis                # List Python API tree

scitex-stats list-python-apis -v             # With docstrings

scitex-stats mcp list-tools                  # List MCP tools

scitex-stats mcp doctor                      # Check server health

scitex-stats mcp start                       # Start MCP server

```

> **[Full CLI reference](https://scitex-stats.readthedocs.io/)**

MCP Server — for AI Agents




AI agents can run statistical tests and format publication-ready results autonomously.

| Tool | Description |

|------|-------------|

| `recommend_tests` | Recommend appropriate tests based on data characteristics |

| `run_test` | Execute a statistical test on provided data |

| `format_results` | Format results in journal style (APA, Nature, etc.) |

| `power_analysis` | Calculate statistical power or required sample size |

| `correct_pvalues` | Apply multiple comparison correction |

| `describe` | Calculate descriptive statistics |

| `effect_size` | Calculate effect size between groups |

| `normality_test` | Test whether data follows normal distribution |

| `posthoc_test` | Run post-hoc pairwise comparisons |

| `p_to_stars` | Convert p-value to significance stars |

*Table 1. MCP tools available for AI agent integration via `scitex-stats mcp start`.*

```bash

scitex-stats mcp start

```

> **[Full MCP specification](https://scitex-stats.readthedocs.io/)**

## Choosing the Right Test



  



*Figure 2. Decision flowchart for choosing a statistical test. Start with your data type, then follow the branches based on number of groups and study design. Brunner-Munzel is recommended as the default for two-group comparisons due to its robustness to unequal variances and non-normality.*

## Available Tests

| Category | Tests |

|----------|-------|

| **Parametric** | t-test (ind, paired, 1-sample), ANOVA (1-way, RM, 2-way) |

| **Nonparametric** | Mann-Whitney U, Wilcoxon, Kruskal-Wallis, Friedman, Brunner-Munzel |

| **Correlation** | Pearson, Spearman, Kendall, Theil-Sen |

| **Categorical** | Chi-squared, Fisher exact, McNemar, Cochran's Q |

| **Normality** | Shapiro-Wilk, Kolmogorov-Smirnov (1-sample, 2-sample) |

*Table 2. All 23 statistical tests organized by category.*

## Lint Rules

Detected by [scitex-linter](https://github.com/ywatanabe1989/scitex-linter) when this package is installed.

| Rule | Severity | Message |

|------|----------|---------|

| `STX-ST001` | warning | `scipy.stats.ttest_ind()` — use `stx.stats.ttest_ind()` for auto effect size + CI |

| `STX-ST002` | warning | `scipy.stats.mannwhitneyu()` — use `stx.stats.mannwhitneyu()` for auto effect size |

| `STX-ST003` | warning | `scipy.stats.pearsonr()` — use `stx.stats.pearsonr()` for auto CI + power |

| `STX-ST004` | warning | `scipy.stats.f_oneway()` — use `stx.stats.anova_oneway()` for post-hoc + effect sizes |

| `STX-ST005` | warning | `scipy.stats.wilcoxon()` — use `stx.stats.wilcoxon()` for auto effect size |

| `STX-ST006` | warning | `scipy.stats.kruskal()` — use `stx.stats.kruskal()` for post-hoc + effect sizes |

## Part of SciTeX

SciTeX Stats is part of [**SciTeX**](https://scitex.ai). When used inside the SciTeX framework, statistical testing integrates with the full pipeline — from data loading through analysis to publication-ready figures:

```python

import scitex

@scitex.session

def main(CONFIG=scitex.INJECTED, plt=scitex.INJECTED):

    # Load data

    data = scitex.io.load("measurements.csv")

    # Run statistical test

    result = scitex.stats.run_test("ttest_ind", data=group1, data2=group2)

    scitex.io.save(result, "stats_result.csv")

    # Visualize with figrecipe (scitex.plt)

    fig, ax = scitex.plt.subplots()

    ax.plot_box([group1, group2], labels=["Control", "Treatment"])

    ax.set_xyt("Group", "Value", f"p = {result['pvalue']:.4f} {result['stars']}")

    scitex.io.save(fig, "comparison.png")  # Saves plot + CSV data

    return 0

```



  



*Figure 3. Example output combining scitex.stats (statistical test) with scitex.plt (publication-ready figure). The box plot shows group comparison with individual data points, significance bracket, p-value, and effect size — all generated from the unified result dictionary.*

The ecosystem modules work together:

| Module | Package | Role |

|--------|---------|------|

| `scitex.stats` | [scitex-stats](https://github.com/ywatanabe1989/scitex-stats) | Statistical testing, effect sizes, power analysis |

| `scitex.plt` | [figrecipe](https://github.com/ywatanabe1989/figrecipe) | Publication-ready figures with auto CSV export |

| `scitex.io` | [scitex-io](https://github.com/ywatanabe1989/scitex-io) | Universal file I/O (30+ formats) |

| `scitex.clew` | [scitex-clew](https://github.com/ywatanabe1989/scitex-clew) | Reproducibility verification via hash DAGs |

The SciTeX system follows the Four Freedoms for Research below, inspired by [the Free Software Definition](https://www.gnu.org/philosophy/free-sw.en.html):

>Four Freedoms for Research

>

>0. The freedom to **run** your research anywhere — your machine, your terms.

>1. The freedom to **study** how every step works — from raw data to final manuscript.

>2. The freedom to **redistribute** your workflows, not just your papers.

>3. The freedom to **modify** any module and share improvements with the community.

>

>AGPL-3.0 — because we believe research infrastructure deserves the same freedoms as the software it runs on.

---

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

https://github.com/ywatanabe1989/scitex-stats

Awesome Lists containing this project

README