scitex-stats 0.2.20

Publication-ready statistical testing framework with 23 tests, effect sizes, power analysis, and MCP server

SciTeX Stats (scitex-stats)

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

Full Documentation · uv pip install "scitex-stats[all]"

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Problem and Solution

#	Problem	Solution
1	Bare scipy.stats returns (statistic, p) — effect size, CI, normality check, power each need manual follow-up calls.	One call, one dict — ss.run_test("ttest_ind", g1, g2) returns statistic, p, Cohen's d, power, and an APA string in a unified result dict.
2	Test selection requires expertise — parametric vs non-parametric, paired vs independent, one-way vs repeated-measures.	Auto-recommend — ss.recommend_tests(StatContext(...)) ranks the appropriate tests from the design alone.
3	APA formatting is manual — every paper spells out t(58) = 2.34, p = .021, d = 0.60 by hand.	result["formatted"] — APA / Nature / LaTeX strings live on the same result dict every test returns.

Quick Start

import numpy as np
import scitex_stats as ss

rng = np.random.default_rng(42)
group1 = rng.normal(0.0, 1.0, 30)
group2 = rng.normal(0.5, 1.0, 30)

# Unified test API — same dict shape for every one of the 23 tests
result = ss.run_test("ttest_ind", data=group1, data2=group2)

assert result["stat_symbol"] == "t"
assert result["effect_size_metric"] == "Cohen's d"
assert result["significant"] is True
print(result["formatted"])
# → t = -3.2101, p = 0.0022, Cohen's d = -0.829, **

Unified result dictionary (every test returns this shape)

{
  "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, **"
}

Installation

uv pip install "scitex-stats[all]"

Per-module extras

Extra	Pulls in
mcp	fastmcp (MCP server for AI agents)
plot	matplotlib (for the optional plotting helpers)
figrecipe	figrecipe (publication figures + auto CSV export)
all	mcp + plot + figrecipe (recommended)
dev	pytest, pytest-cov, nbconvert, ipykernel, + every optional dep so the test suite runs
docs	Sphinx + RTD theme + myst-parser (docs build only)

uv pip install "scitex-stats[mcp]"        # MCP server only
uv pip install -e ".[dev]"                # editable install for contributors
pip install scitex-stats[all]             # pip works too, just slower

How it works

1. Describe the design, recommend the test

StatContext captures the experimental design — number of groups, sample sizes, outcome type, paired vs between. recommend_tests ranks the appropriate tests from that context alone, before any data is touched.

ctx = ss.StatContext(
    n_groups=2, sample_sizes=[30, 30],
    outcome_type="continuous", design="between", paired=False,
)
ss.recommend_tests(ctx, top_k=3)
# → ['ttest_ind', 'welch_t', 'brunner_munzel']

2. Run the test, get the unified result

run_test is the single dispatcher for all 23 tests. The same result dict shape (statistic, pvalue, effect_size, power, formatted, …) makes the downstream code test-agnostic.

flowchart TB
    Data[Raw arrays / DataFrame] --> Ctx[StatContext]
    Ctx --> Rec[recommend_tests]
    Rec --> Run[run_test]
    Run --> ES[effect_sizes]
    Run --> Pw[power]
    Run --> Res[Unified result dict]
    Res --> Corr[correct: FDR/Bonferroni/Holm]
    Res --> Post[posthoc: Tukey/Dunn/Nemenyi]
    Res --> Fmt[format: APA/Nature/LaTeX]
    Fmt --> Pub[Publication-ready string]

    subgraph Surfaces ["Four surfaces — same engine"]
        Py[Python API]
        Cli[CLI]
        Mcp[MCP server]
        Sk[Skills]
    end
    Py -.-> Run
    Cli -.-> Run
    Mcp -.-> Run
    Sk -.-> Run

    style Pub fill:#27ae60,stroke:#2c3e50,color:#fff
    style Res fill:#4a90d9,stroke:#2c3e50,color:#fff

_{Figure 1. Data flow and the four surfaces (Python, CLI, MCP, Skills) that share the same run_test engine. Every interface emits the unified result dict, which downstream formatters and corrections consume.}

3. Effect sizes, power, corrections

Every numeric result is built from the same primitives. Use them standalone when the dispatcher's defaults aren't quite right.

from scitex_stats import effect_sizes, power, correct

effect_sizes.cohens_d(group1, group2)            # → -0.829
power.sample_size_ttest(effect_size=0.5,
                        alpha=0.05, power=0.8)   # → required n per group
correct.correct_fdr(results, alpha=0.05,
                    method="bh")                 # BH adjusted p-values

4. Linter for migration and hooks

scitex-stats ships 6 stats-specific lint rules (STX-ST001..006). They are detected automatically by scitex-dev's linter, already a hard dependency — no extra install.

scitex-dev linter check-files src/                # lint a tree
scitex-dev linter list-rules --category stats     # show live rule definitions

Rule reference (STX-ST001..006)

Rule	Severity	Trigger
STX-ST001	warning	scipy.stats.ttest_ind() — use ss.run_test("ttest_ind", ...) for auto effect size + CI + power
STX-ST002	warning	scipy.stats.mannwhitneyu() — use ss.run_test("mannwhitneyu", ...) for auto effect size
STX-ST003	warning	scipy.stats.pearsonr() — use ss.run_test("pearsonr", ...) for auto CI + power
STX-ST004	warning	scipy.stats.f_oneway() — use ss.run_test("anova_oneway", ...) for post-hoc + effect sizes
STX-ST005	warning	scipy.stats.wilcoxon() — use ss.run_test("wilcoxon", ...) for auto effect size
STX-ST006	warning	scipy.stats.kruskal() — use ss.run_test("kruskal", ...) for post-hoc + effect sizes

5. Etc.

Descriptive statistics, post-hoc, normality checks

from scitex_stats import describe, posthoc

describe(data)                              # mean, sd, median, IQR, skew, kurtosis
posthoc.posthoc_tukey([g1, g2, g3])         # pairwise Tukey HSD
ss.run_test("shapiro", data=group1)         # normality check, same result dict shape

Available Tests

flowchart LR
    All[23 tests] --> P[Parametric]
    All --> N[Nonparametric]
    All --> C[Correlation]
    All --> Cat[Categorical]
    All --> Norm[Normality]

    P --> P1[t-test ind / paired / 1-samp]
    P --> P2[ANOVA 1-way / RM / 2-way]

    N --> N1[Mann-Whitney U]
    N --> N2[Wilcoxon]
    N --> N3[Kruskal-Wallis]
    N --> N4[Friedman]
    N --> N5[Brunner-Munzel]

    C --> C1[Pearson]
    C --> C2[Spearman]
    C --> C3[Kendall]
    C --> C4[Theil-Sen]

    Cat --> Cat1[Chi-squared]
    Cat --> Cat2[Fisher exact]
    Cat --> Cat3[McNemar]
    Cat --> Cat4[Cochran's Q]

    Norm --> Norm1[Shapiro-Wilk]
    Norm --> Norm2[Kolmogorov-Smirnov 1-samp]
    Norm --> Norm3[Kolmogorov-Smirnov 2-samp]

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

_{Figure 2. The 23 tests grouped by family. Every leaf is callable through the same run_test(name, ...) dispatcher and returns the unified result dict (Figure 1).}

flowchart TB
    Start([Choose a test]) --> Outcome{Outcome type?}

    Outcome -->|Continuous| K{# groups?}
    Outcome -->|Ordinal / ranked| K
    Outcome -->|Categorical / counts| Cat{Design?}
    Outcome -->|Correlation| Corr{Variable types?}

    K -->|1| OneSamp{Normal?}
    OneSamp -->|Yes| OS1[t-test 1-sample]
    OneSamp -->|No| OS2[Wilcoxon signed-rank]

    K -->|2| Two{Paired?}
    Two -->|No| TwoInd{Normal + equal var?}
    Two -->|Yes| TwoP{Normal diffs?}
    TwoInd -->|Yes| TI1[t-test ind / Welch]
    TwoInd -->|No| TI2["Brunner-Munzel <b>★ default</b>"]
    TwoP -->|Yes| TP1[t-test paired]
    TwoP -->|No| TP2[Wilcoxon signed-rank]

    K -->|3+| Many{Design?}
    Many -->|Between| MB{Normal + equal var?}
    Many -->|Within| MW{Normal?}
    Many -->|2-factor| M2[ANOVA 2-way]
    MB -->|Yes| MB1[ANOVA 1-way]
    MB -->|No| MB2[Kruskal-Wallis]
    MW -->|Yes| MW1[ANOVA repeated-measures]
    MW -->|No| MW2[Friedman]

    Cat -->|"2×2 unpaired"| Cat1[Fisher exact]
    Cat -->|"larger contingency"| Cat2[Chi-squared]
    Cat -->|"2×2 paired"| Cat3[McNemar]
    Cat -->|"3+ repeated binary"| Cat4["Cochran's Q"]

    Corr -->|Continuous + linear| Co1[Pearson]
    Corr -->|Monotonic / ranks| Co2[Spearman]
    Corr -->|Small n, ties| Co3[Kendall τ]
    Corr -->|With outliers| Co4[Theil-Sen]

    style TI2 fill:#27ae60,stroke:#2c3e50,color:#fff
    style Start fill:#4a90d9,stroke:#2c3e50,color:#fff

_{Figure 3. Decision flowchart for choosing a statistical test. Start from outcome type, branch by number of groups and study design. Brunner-Munzel (★) is the recommended default for two-group continuous comparisons — robust to unequal variances and non-normality.}

Examples

Three runnable notebooks under examples/ — each one executes end-to-end in CI and is the canonical reference for its workflow.

Notebook	Workflow
01_basic_ttest.ipynb	run_test("ttest_ind", ...) → unified result dict → APA string
02_test_recommendation.ipynb	StatContext → recommend_tests → top recommendation through run_test
03_multiple_comparison.ipynb	Family of comparisons → correct.correct_fdr (Benjamini-Hochberg)

# Re-execute every notebook in place (refreshes outputs)
bash examples/00_run_all.sh

Four Interfaces

Python API ⭐⭐⭐

import scitex_stats as ss
from scitex_stats import effect_sizes, power, correct, posthoc

ss.run_test("ttest_ind", data=g1, data2=g2)             # 23 tests, one dispatcher
ss.recommend_tests(ss.StatContext(n_groups=2, ...))     # design-driven test selection
effect_sizes.cohens_d(g1, g2)                           # standalone effect size
power.sample_size_ttest(effect_size=0.5,
                        alpha=0.05, power=0.8)          # power / sample size
correct.correct_fdr(results, alpha=0.05, method="bh")   # multiple-comparison correction
posthoc.posthoc_tukey([g1, g2, g3])                     # post-hoc pairwise tests

Full API reference

CLI Commands ⭐

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

MCP Server ⭐⭐

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

Tool	Description
recommend_tests	Recommend appropriate tests from a StatContext
run_test	Execute any of the 23 statistical tests
format_results	Format results in journal style (APA, Nature, LaTeX)
power_analysis	Compute statistical power or required sample size
correct_pvalues	Apply multiple-comparison correction
describe	Compute descriptive statistics
effect_size	Compute effect size between groups
normality_test	Test whether data follows a normal distribution
posthoc_test	Run post-hoc pairwise comparisons
p_to_stars	Convert p-value to significance stars

scitex-stats mcp start

Full MCP specification

Skills ⭐⭐

Skills are workflow-oriented guides AI agents query to discover package capabilities and usage patterns.

scitex-stats skills list                              # list available skill pages
scitex-stats skills get SKILL                         # show a skill page
scitex-dev skills export --package scitex-stats       # export to Claude Code

Skill	Content
quick-start	Basic usage and core patterns
test-catalog	All 23 statistical tests with categories
effect-sizes	Effect size measures and interpretation
workflows	Common analysis patterns
cli-reference	CLI commands
mcp-tools	MCP tools for AI agents

Also available via MCP: stats_skills_list() / stats_skills_get(name).

Part of SciTeX

scitex-stats is part of SciTeX. Install via the umbrella with pip install scitex[stats] to use as scitex.stats (Python) or scitex stats ... (CLI).

import scitex

@scitex.session
def main(CONFIG=scitex.INJECTED, plt=scitex.INJECTED):
    data = scitex.io.load("measurements.csv")
    result = scitex.stats.run_test("ttest_ind", data=data["g1"], data2=data["g2"])
    scitex.io.save(result, "stats_result.csv")

    fig, ax = scitex.plt.subplots()
    ax.plot_box([data["g1"], data["g2"]], 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

scitex.stats delegates to scitex_stats — same API, same registry.

The ecosystem modules compose:

Module	Package	Role
scitex.stats	scitex-stats	Statistical testing, effect sizes, power analysis
scitex.plt	figrecipe	Publication-ready figures with auto CSV export
scitex.io	scitex-io	Universal file I/O (30+ formats)
scitex.clew	scitex-clew	Reproducibility verification via hash DAGs

The SciTeX system follows the Four Freedoms for Research, inspired by the Free Software Definition:

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.

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