xjtulyc/statistical-testing

Statistical Testing

Choose and execute the right statistical test for your data. This skill covers normality checks, parametric and non-parametric tests, effect size computation, and multiple comparison correction.

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Decision Tree

| Scenario | Groups | Normal? | Recommended Test | |---|---|---|---| | Compare means | 2 independent | Yes | Independent t-test | | Compare means | 2 independent | No | Mann-Whitney U | | Compare means | 2 paired | Yes | Paired t-test | | Compare means | 2 paired | No | Wilcoxon signed-rank | | Compare means | ≥3 independent | Yes | One-way ANOVA | | Compare means | ≥3 independent | No | Kruskal-Wallis | | Compare means | ≥3 repeated | Yes | Repeated-measures ANOVA | | Compare means | ≥3 repeated | No | Friedman test | | Association | 2 categorical | Expected ≥5 | Chi-square | | Association | 2 categorical | Expected <5 | Fisher exact | | Correlation | 2 continuous | Both normal | Pearson r | | Correlation | 2 continuous | Non-normal | Spearman ρ |

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Setup

pip install scipy numpy pandas statsmodels pingouin

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Core Implementation

"""
statistical_testing.py
Complete statistical testing toolkit with automatic test selection.
"""

import warnings
import numpy as np
import pandas as pd
from scipy import stats
from statsmodels.stats import multitest
from statsmodels.stats.power import TTestIndPower, FTestAnovaPower
from typing import Union, Optional, Tuple, List, Dict, Any


# ─────────────────────────────────────────────
# 1. Normality Tests
# ─────────────────────────────────────────────

def test_normality(
    data: np.ndarray,
    alpha: float = 0.05,
    verbose: bool = True,
) -> Dict[str, Any]:
    """
    Run Shapiro-Wilk, Kolmogorov-Smirnov, and D'Agostino-Pearson normality tests.

    Parameters
    ----------
    data : array-like
        1-D numeric data.
    alpha : float
        Significance level.
    verbose : bool
        Print a summary.

    Returns
    -------
    dict with keys: shapiro, ks, dagostino, is_normal
    """
    data = np.asarray(data, dtype=float)
    data = data[~np.isnan(data)]
    n = len(data)

    results: Dict[str, Any] = {}

    # Shapiro-Wilk (best for n < 5000)
    if n < 5000:
        stat_sw, p_sw = stats.shapiro(data)
        results["shapiro"] = {"statistic": stat_sw, "p_value": p_sw, "normal": p_sw > alpha}
    else:
        results["shapiro"] = {"statistic": None, "p_value": None, "normal": None,
                              "note": "n>=5000; skipped Shapiro-Wilk"}

    # Kolmogorov-Smirnov against fitted normal
    mu, sigma = np.mean(data), np.std(data, ddof=1)
    stat_ks, p_ks = stats.kstest(data, "norm", args=(mu, sigma))
    results["ks"] = {"statistic": stat_ks, "p_value": p_ks, "normal": p_ks > alpha}

    # D'Agostino-Pearson (needs n >= 20)
    if n >= 20:
        stat_da, p_da = stats.normaltest(data)
        results["dagostino"] = {"statistic": stat_da, "p_value": p_da, "normal": p_da > alpha}
    else:
        results["dagostino"] = {"statistic": None, "p_value": None, "normal": None,
                                "note": "n<20; skipped D'Agostino"}

    # Consensus: normal if at least 2 out of 3 available tests agree
    votes = [v["normal"] for v in results.values() if isinstance(v, dict) and v["normal"] is not None]
    results["is_normal"] = sum(votes) >= (len(votes) / 2)
    results["n"] = n

    if verbose:
        print(f"Normality tests (n={n}, alpha={alpha})")
        for test_name, res in results.items():
            if isinstance(res, dict) and "p_value" in res and res["p_value"] is not None:
                verdict = "NORMAL" if res["normal"] else "NON-NORMAL"
                print(f"  {test_name:15s}: stat={res['statistic']:.4f}, p={res['p_value']:.4f}  [{verdict}]")
        print(f"  Consensus: {'NORMAL' if results['is_normal'] else 'NON-NORMAL'}\n")

    return results


# ─────────────────────────────────────────────
# 2. Effect Sizes
# ─────────────────────────────────────────────

def cohens_d(group1: np.ndarray, group2: np.ndarray) -> float:
    """Cohen's d for two independent groups."""
    g1, g2 = np.asarray(group1), np.asarray(group2)
    n1, n2 = len(g1), len(g2)
    pooled_sd = np.sqrt(((n1 - 1) * np.var(g1, ddof=1) + (n2 - 1) * np.var(g2, ddof=1)) / (n1 + n2 - 2))
    return (np.mean(g1) - np.mean(g2)) / pooled_sd


def eta_squared(f_statistic: float, df_between: int, df_within: int) -> float:
    """Eta-squared from F-statistic for one-way ANOVA."""
    ss_between = f_statistic * df_between
    ss_total = ss_between + df_within
    return ss_between / ss_total


def cramers_v(contingency_table: np.ndarray) -> float:
    """Cramér's V for association in a contingency table."""
    chi2 = stats.chi2_contingency(contingency_table, correction=False)[0]
    n = contingency_table.sum()
    min_dim = min(contingency_table.shape) - 1
    return np.sqrt(chi2 / (n * min_dim))


def interpret_effect_size(d: float, measure: str = "cohens_d") -> str:
    """Return a qualitative label for an effect size."""
    thresholds = {
        "cohens_d":   [(0.2, "small"), (0.5, "medium"), (0.8, "large")],
        "eta_squared": [(0.01, "small"), (0.06, "medium"), (0.14, "large")],
        "cramers_v":  [(0.1, "small"), (0.3, "medium"), (0.5, "large")],
    }
    d = abs(d)
    for threshold, label in thresholds.get(measure, []):
        if d < threshold:
            return label
    return "large"


# ─────────────────────────────────────────────
# 3. Automatic Test Selection
# ─────────────────────────────────────────────

def run_comparison(
    *groups: np.ndarray,
    paired: bool = False,
    alpha: float = 0.05,
    verbose: bool = True,
) -> Dict[str, Any]:
    """
    Automatically select and run the appropriate comparison test.

    Parameters
    ----------
    *groups : array-like
        Two or more numeric arrays (one per group).
    paired : bool
        Whether observations are paired / repeated measures.
    alpha : float
        Significance level.
    verbose : bool
        Print result summary.

    Returns
    -------
    dict with keys: test_name, statistic, p_value, effect_size, significant
    """
    groups = [np.asarray(g, dtype=float) for g in groups]
    k = len(groups)

    if k < 2:
        raise ValueError("Need at least 2 groups.")

    # Check normality for each group
    all_normal = all(test_normality(g, alpha=alpha, verbose=False)["is_normal"] for g in groups)

    result: Dict[str, Any] = {}

    if k == 2:
        g1, g2 = groups[0], groups[1]
        if paired:
            if all_normal:
                stat, p = stats.ttest_rel(g1, g2)
                result["test_name"] = "Paired t-test"
                d = np.mean(g1 - g2) / np.std(g1 - g2, ddof=1)
                result["effect_size"] = {"cohens_d": round(d, 4)}
            else:
                stat, p = stats.wilcoxon(g1, g2)
                result["test_name"] = "Wilcoxon signed-rank"
                result["effect_size"] = {"rank_biserial": round(1 - (2 * stat) / (len(g1) * (len(g1) + 1)), 4)}
        else:
            # Levene's test for equal variances
            _, p_levene = stats.levene(g1, g2)
            equal_var = p_levene > alpha

            if all_normal:
                stat, p = stats.ttest_ind(g1, g2, equal_var=equal_var)
                result["test_name"] = f"Independent t-test ({'equal' if equal_var else 'Welch'} variance)"
                d = cohens_d(g1, g2)
                result["effect_size"] = {"cohens_d": round(d, 4),
                                         "interpretation": interpret_effect_size(d)}
            else:
                stat, p = stats.mannwhitneyu(g1, g2, alternative="two-sided")
                result["test_name"] = "Mann-Whitney U"
                n1, n2 = len(g1), len(g2)
                rb = 1 - (2 * stat) / (n1 * n2)
                result["effect_size"] = {"rank_biserial_r": round(rb, 4)}
    else:
        # k >= 3
        if paired:
            if all_normal:
                # Repeated-measures via pingouin if available, else warn
                try:
                    import pingouin as pg
                    df_long = pd.DataFrame({
                        "value": np.concatenate(groups),
                        "group": np.repeat(np.arange(k), [len(g) for g in groups]),
                        "subject": np.tile(np.arange(len(groups[0])), k),
                    })
                    aov = pg.rm_anova(data=df_long, dv="value", within="group", subject="subject")
                    stat = aov["F"].iloc[0]
                    p = aov["p-unc"].iloc[0]
                    result["test_name"] = "Repeated-measures ANOVA (pingouin)"
                    result["effect_size"] = {"eta_squared": round(aov["np2"].iloc[0], 4)}
                except ImportError:
                    warnings.warn("pingouin not installed; falling back to Friedman test.")
                    stat, p = stats.friedmanchisquare(*groups)
                    result["test_name"] = "Friedman test (fallback)"
                    result["effect_size"] = {}
            else:
                stat, p = stats.friedmanchisquare(*groups)
                result["test_name"] = "Friedman test"
                result["effect_size"] = {}
        else:
            if all_normal:
                stat, p = stats.f_oneway(*groups)
                result["test_name"] = "One-way ANOVA"
                total_n = sum(len(g) for g in groups)
                df_between = k - 1
                df_within = total_n - k
                es = eta_squared(stat, df_between, df_within)
                result["effect_size"] = {"eta_squared": round(es, 4),
                                         "interpretation": interpret_effect_size(es, "eta_squared")}
            else:
                stat, p = stats.kruskal(*groups)
                result["test_name"] = "Kruskal-Wallis H"
                result["effect_size"] = {}

    result["statistic"] = round(float(stat), 4)
    result["p_value"] = round(float(p), 6)
    result["significant"] = p < alpha
    result["alpha"] = alpha

    if verbose:
        print(f"Test: {result['test_name']}")
        print(f"  Statistic = {result['statistic']}, p = {result['p_value']}")
        print(f"  Significant at alpha={alpha}: {result['significant']}")
        if result.get("effect_size"):
            print(f"  Effect size: {result['effect_size']}")
        print()

    return result


# ─────────────────────────────────────────────
# 4. Multiple Comparison Correction
# ─────────────────────────────────────────────

def correct_pvalues(
    p_values: List[float],
    method: str = "fdr_bh",
    alpha: float = 0.05,
) -> pd.DataFrame:
    """
    Apply multiple comparison correction.

    Parameters
    ----------
    p_values : list of float
    method : str
        One of: bonferroni, holm, fdr_bh (Benjamini-Hochberg),
        fdr_by (Benjamini-Yekutieli), sidak
    alpha : float

    Returns
    -------
    DataFrame with original p-values, corrected p-values, and rejection flags.
    """
    reject, p_corrected, _, _ = multitest.multipletests(p_values, alpha=alpha, method=method)
    return pd.DataFrame({
        "p_original": p_values,
        "p_corrected": p_corrected,
        "reject_H0": reject,
        "method": method,
    })


# ─────────────────────────────────────────────
# 5. Power Analysis
# ─────────────────────────────────────────────

def power_analysis_ttest(
    effect_size: float = 0.5,
    alpha: float = 0.05,
    power: float = 0.80,
    n_per_group: Optional[int] = None,
) -> Dict[str, float]:
    """
    Two-sample t-test power analysis.
    Provide any three of (effect_size, alpha, power, n_per_group) to solve for the fourth.
    """
    analysis = TTestIndPower()
    if n_per_group is None:
        n = analysis.solve_power(effect_size=effect_size, alpha=alpha, power=power, ratio=1.0)
        return {"required_n_per_group": np.ceil(n), "effect_size": effect_size,
                "alpha": alpha, "power": power}
    else:
        achieved_power = analysis.solve_power(effect_size=effect_size, alpha=alpha,
                                               nobs1=n_per_group, ratio=1.0)
        return {"n_per_group": n_per_group, "effect_size": effect_size,
                "alpha": alpha, "achieved_power": round(achieved_power, 4)}


def chi_square_test_with_effect(
    contingency: np.ndarray,
    alpha: float = 0.05,
) -> Dict[str, Any]:
    """Chi-square test with Cramér's V effect size."""
    chi2, p, dof, expected = stats.chi2_contingency(contingency)
    v = cramers_v(contingency)
    result = {
        "test_name": "Chi-square" if expected.min() >= 5 else "Fisher exact (recommended)",
        "chi2": round(chi2, 4),
        "p_value": round(p, 6),
        "dof": dof,
        "cramers_v": round(v, 4),
        "significant": p < alpha,
    }
    if expected.min() < 5:
        if contingency.shape == (2, 2):
            _, p_fisher = stats.fisher_exact(contingency)
            result["fisher_p"] = round(p_fisher, 6)
    return result

---

Example 1 — Comparing Three Treatment Groups

import numpy as np
from statistical_testing import run_comparison, correct_pvalues, power_analysis_ttest

rng = np.random.default_rng(42)

# Simulate three treatment groups (non-normal distributions)
control   = rng.exponential(scale=5, size=40)
treatment1 = rng.exponential(scale=7, size=38)
treatment2 = rng.exponential(scale=9, size=42)

# Automatic test selection
result = run_comparison(control, treatment1, treatment2, alpha=0.05)
# → Kruskal-Wallis H (non-normal data detected automatically)

# If significant, run post-hoc pairwise tests with FDR correction
from scipy.stats import mannwhitneyu

pairs = [
    ("control vs t1", mannwhitneyu(control, treatment1, alternative="two-sided").pvalue),
    ("control vs t2", mannwhitneyu(control, treatment2, alternative="two-sided").pvalue),
    ("t1 vs t2",      mannwhitneyu(treatment1, treatment2, alternative="two-sided").pvalue),
]

labels, raw_p = zip(*pairs)
correction_df = correct_pvalues(list(raw_p), method="fdr_bh")
correction_df.index = labels
print(correction_df)

---

Example 2 — Two-Group Comparison with Power Report

import numpy as np
from statistical_testing import (
    test_normality, run_comparison, cohens_d, power_analysis_ttest
)

rng = np.random.default_rng(0)

pre  = rng.normal(loc=100, scale=15, size=30)
post = pre + rng.normal(loc=8, scale=10, size=30)   # ~0.5 SD improvement

# Step 1: Check normality of the difference
diff = post - pre
norm_result = test_normality(diff)

# Step 2: Run the appropriate test
result = run_comparison(pre, post, paired=True)

# Step 3: Retrospective power
d = result["effect_size"].get("cohens_d", cohens_d(pre, post))
power_report = power_analysis_ttest(effect_size=abs(d), alpha=0.05, n_per_group=30)
print(f"Achieved power: {power_report['achieved_power']:.2f}")

# Step 4: How many participants for 90% power?
needed = power_analysis_ttest(effect_size=abs(d), alpha=0.05, power=0.90)
print(f"N needed for 90% power: {int(needed['required_n_per_group'])}")

---

Categorical Data Example

import numpy as np
from statistical_testing import chi_square_test_with_effect, correct_pvalues

# 2x2 contingency: treatment vs outcome
table = np.array([[45, 15],
                  [30, 30]])

result = chi_square_test_with_effect(table)
print(result)
# {'test_name': 'Chi-square', 'chi2': ..., 'p_value': ..., 'cramers_v': ..., 'significant': True}

# 3x3 contingency across three centres
multi_table = np.array([[50, 20, 10],
                        [40, 25, 15],
                        [35, 30, 20]])
result3 = chi_square_test_with_effect(multi_table)
print(result3)

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Quick Reference: Effect Size Benchmarks

| Measure | Small | Medium | Large | |---|---|---|---| | Cohen's d | 0.2 | 0.5 | 0.8 | | η² (eta-squared) | 0.01 | 0.06 | 0.14 | | Cramér's V (2×2) | 0.1 | 0.3 | 0.5 | | Pearson r | 0.1 | 0.3 | 0.5 |

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Multiple Comparison Methods

| Method | Controls | Best For | |---|---|---| | Bonferroni | Family-wise error rate (FWER) | Few comparisons, strict control | | Holm | FWER (less conservative) | General use | | Benjamini-Hochberg (FDR) | False discovery rate | Many comparisons (genomics, etc.) | | Benjamini-Yekutieli | FDR under dependence | Correlated tests |

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Common Pitfalls

• Assuming normality without testing: Always run normality checks, especially for n < 30.
• Ignoring equal-variance assumption: Run Levene's test before independent t-test.
• Multiple comparisons inflation: Any time you run ≥3 tests on the same dataset, apply correction.
• Over-relying on p < 0.05: Always report effect sizes alongside p-values.
• Wrong test for paired data: Pre/post measurements are paired; use paired tests.

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Environment Variables

No API keys required. All computation is local.

# Optional: set random seed for reproducibility in scripts
export STATS_RANDOM_SEED=42