Examples

All examples are available in the examples/ directory and produce both human-readable (.txt) and structured (.json) output.

Example 1: Basic t-test

Figure 1. Box plot with individual data points, significance bracket, p-value, and effect size — generated from the unified result dictionary.

import scitex_stats as ss
import numpy as np

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

result = ss.run_test("ttest_ind", data=group1, data2=group2)
print(result["formatted"])
# t = -2.923, p = 0.0049, Cohen's d = -0.755, **

Output:

Independent t-test
========================================
t-statistic: -2.9233
p-value: 0.0049
Effect size (Cohen's d): -0.7548
Formatted: t = -2.923, p = 0.0049, Cohen's d = -0.755, **

Example 2: Automatic Test Recommendation

import scitex_stats as ss

ctx = ss.StatContext(
    n_groups=2,
    sample_sizes=[30, 32],
    outcome_type="continuous",
    design="between",
    paired=False,
    has_control_group=False,
    n_factors=1,
)
recs = ss.recommend_tests(ctx, top_k=5)
print(recs)
# ['brunner_munzel', 'ttest_ind', 'mannwhitneyu']

Output:

Test Recommendations
========================================
  1. brunner_munzel
  2. ttest_ind
  3. mannwhitneyu

Example 3: Multiple Comparison Correction

from scitex_stats import correct

results = [
    {"pvalue": 0.01, "var_x": "A", "var_y": "B"},
    {"pvalue": 0.04, "var_x": "A", "var_y": "C"},
    {"pvalue": 0.03, "var_x": "A", "var_y": "D"},
    {"pvalue": 0.20, "var_x": "B", "var_y": "C"},
    {"pvalue": 0.005, "var_x": "B", "var_y": "D"},
    {"pvalue": 0.08, "var_x": "C", "var_y": "D"},
]

corrected = correct.correct_fdr(results, alpha=0.05, method="bh")

Output:

Multiple Comparison Correction (FDR-BH)
==================================================
Comparison   Original   Adjusted   Rejected
---------------------------------------------
       AvB     0.0100     0.0300        Yes
       AvC     0.0400     0.0600         No
       AvD     0.0300     0.0600         No
       BvC     0.2000     0.2000         No
       BvD     0.0050     0.0300        Yes
       CvD     0.0800     0.0960         No