Quick Start

Python API

import scitex_stats as ss
import numpy as np

# Generate sample data
rng = np.random.default_rng(42)
group1 = rng.normal(0, 1, 30)
group2 = rng.normal(0.5, 1, 30)

# Step 1: 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)
print(recs)  # ['brunner_munzel', 'ttest_ind', 'mannwhitneyu']

# Step 2: Run a test
result = ss.run_test("ttest_ind", data=group1, data2=group2)

# Step 3: APA-formatted output
print(result["formatted"])
# t = -2.923, p = 0.0049, Cohen's d = -0.755, **

# Access individual fields
print(f"H0: {result['H0']}")           # μ(x) = μ(y)
print(f"Effect: {result['effect_size_interpretation']}")  # medium

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, alpha=0.05)

Post-hoc Tests

from scitex_stats import posthoc
results = posthoc.posthoc_tukey(groups)

CLI

# List available Python APIs
scitex-stats list-python-apis -v

# List MCP tools
scitex-stats mcp list-tools -v

MCP Server

# Start the MCP server
scitex-stats mcp start

# Check health
scitex-stats mcp doctor