Statistical Analysis

Overview

Statistical analysis is a systematic process for testing hypotheses and quantifying relationships. Conduct hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, and Bayesian analyses with assumption checks and APA reporting. Apply this skill for academic research.

When to Use This Skill

This skill should be used when:

• Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square)

• Performing regression or correlation analyses

• Running Bayesian statistical analyses

• Checking statistical assumptions and diagnostics

• Calculating effect sizes and conducting power analyses

• Reporting statistical results in APA format

• Analyzing experimental or observational data for research

Core Capabilities

1. Test Selection and Planning

• Choose appropriate statistical tests based on research questions and data characteristics

• Conduct a priori power analyses to determine required sample sizes

• Plan analysis strategies including multiple comparison corrections

2. Assumption Checking

• Automatically verify all relevant assumptions before running tests

• Provide diagnostic visualizations (Q-Q plots, residual plots, box plots)

• Recommend remedial actions when assumptions are violated

3. Statistical Testing

• Hypothesis testing: t-tests, ANOVA, chi-square, non-parametric alternatives

• Regression: linear, multiple, logistic, with diagnostics

• Correlations: Pearson, Spearman, with confidence intervals

• Bayesian alternatives: Bayesian t-tests, ANOVA, regression with Bayes Factors

4. Effect Sizes and Interpretation

• Calculate and interpret appropriate effect sizes for all analyses

• Provide confidence intervals for effect estimates

• Distinguish statistical from practical significance

5. Professional Reporting

• Generate APA-style statistical reports

• Create publication-ready figures and tables

• Provide complete interpretation with all required statistics

Workflow Decision Tree

Use this decision tree to determine your analysis path:

START │ ├─ Need to SELECT a statistical test? │ └─ YES → See "Test Selection Guide" │ └─ NO → Continue │ ├─ Ready to check ASSUMPTIONS? │ └─ YES → See "Assumption Checking" │ └─ NO → Continue │ ├─ Ready to run ANALYSIS? │ └─ YES → See "Running Statistical Tests" │ └─ NO → Continue │ └─ Need to REPORT results? └─ YES → See "Reporting Results"

Test Selection Guide

Quick Reference: Choosing the Right Test

Use references/test_selection_guide.md for comprehensive guidance. Quick reference:

Comparing Two Groups:

• Independent, continuous, normal → Independent t-test

• Independent, continuous, non-normal → Mann-Whitney U test

• Paired, continuous, normal → Paired t-test

• Paired, continuous, non-normal → Wilcoxon signed-rank test

• Binary outcome → Chi-square or Fisher's exact test

Comparing 3+ Groups:

• Independent, continuous, normal → One-way ANOVA

• Independent, continuous, non-normal → Kruskal-Wallis test

• Paired, continuous, normal → Repeated measures ANOVA

• Paired, continuous, non-normal → Friedman test

Relationships:

• Two continuous variables → Pearson (normal) or Spearman correlation (non-normal)

• Continuous outcome with predictor(s) → Linear regression

• Binary outcome with predictor(s) → Logistic regression

Bayesian Alternatives: All tests have Bayesian versions that provide:

• Direct probability statements about hypotheses

• Bayes Factors quantifying evidence

• Ability to support null hypothesis

• See references/bayesian_statistics.md

Assumption Checking

Systematic Assumption Verification

ALWAYS check assumptions before interpreting test results.

Use the provided scripts/assumption_checks.py module for automated checking:

from scripts.assumption_checks import comprehensive_assumption_check

Comprehensive check with visualizations

results = comprehensive_assumption_check( data=df, value_col='score', group_col='group', # Optional: for group comparisons alpha=0.05 )

This performs:

• Outlier detection (IQR and z-score methods)

• Normality testing (Shapiro-Wilk test + Q-Q plots)

• Homogeneity of variance (Levene's test + box plots)

• Interpretation and recommendations

Individual Assumption Checks

For targeted checks, use individual functions:

from scripts.assumption_checks import ( check_normality, check_normality_per_group, check_homogeneity_of_variance, check_linearity, detect_outliers )

Example: Check normality with visualization

result = check_normality( data=df['score'], name='Test Score', alpha=0.05, plot=True ) print(result['interpretation']) print(result['recommendation'])

What to Do When Assumptions Are Violated

Normality violated:

• Mild violation + n > 30 per group → Proceed with parametric test (robust)

• Moderate violation → Use non-parametric alternative

• Severe violation → Transform data or use non-parametric test

Homogeneity of variance violated:

• For t-test → Use Welch's t-test

• For ANOVA → Use Welch's ANOVA or Brown-Forsythe ANOVA

• For regression → Use robust standard errors or weighted least squares

Linearity violated (regression):

• Add polynomial terms

• Transform variables

• Use non-linear models or GAM

See references/assumptions_and_diagnostics.md for comprehensive guidance.

Running Statistical Tests

Python Libraries

Primary libraries for statistical analysis:

• scipy.stats: Core statistical tests

• statsmodels: Advanced regression and diagnostics

• pingouin: User-friendly statistical testing with effect sizes

• pymc: Bayesian statistical modeling

• arviz: Bayesian visualization and diagnostics

Example Analyses

T-Test with Complete Reporting

import pingouin as pg import numpy as np

Run independent t-test

result = pg.ttest(group_a, group_b, correction='auto')

Extract results

t_stat = result['T'].values[0] df = result['dof'].values[0] p_value = result['p-val'].values[0] cohens_d = result['cohen-d'].values[0] ci_lower = result['CI95%'].values[0][0] ci_upper = result['CI95%'].values[0][1]

Report

print(f"t({df:.0f}) = {t_stat:.2f}, p = {p_value:.3f}") print(f"Cohen's d = {cohens_d:.2f}, 95% CI [{ci_lower:.2f}, {ci_upper:.2f}]")

ANOVA with Post-Hoc Tests

import pingouin as pg

One-way ANOVA

aov = pg.anova(dv='score', between='group', data=df, detailed=True) print(aov)

If significant, conduct post-hoc tests

if aov['p-unc'].values[0] < 0.05: posthoc = pg.pairwise_tukey(dv='score', between='group', data=df) print(posthoc)

Effect size

eta_squared = aov['np2'].values[0] # Partial eta-squared print(f"Partial η² = {eta_squared:.3f}")

Linear Regression with Diagnostics

import statsmodels.api as sm from statsmodels.stats.outliers_influence import variance_inflation_factor

Fit model

X = sm.add_constant(X_predictors) # Add intercept model = sm.OLS(y, X).fit()

Summary

print(model.summary())

Check multicollinearity (VIF)

vif_data = pd.DataFrame() vif_data["Variable"] = X.columns vif_data["VIF"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])] print(vif_data)

Check assumptions

residuals = model.resid fitted = model.fittedvalues

Residual plots

import matplotlib.pyplot as plt fig, axes = plt.subplots(2, 2, figsize=(12, 10))

Residuals vs fitted

axes[0, 0].scatter(fitted, residuals, alpha=0.6) axes[0, 0].axhline(y=0, color='r', linestyle='--') axes[0, 0].set_xlabel('Fitted values') axes[0, 0].set_ylabel('Residuals') axes[0, 0].set_title('Residuals vs Fitted')

Q-Q plot

from scipy import stats stats.probplot(residuals, dist="norm", plot=axes[0, 1]) axes[0, 1].set_title('Normal Q-Q')

Scale-Location

axes[1, 0].scatter(fitted, np.sqrt(np.abs(residuals / residuals.std())), alpha=0.6) axes[1, 0].set_xlabel('Fitted values') axes[1, 0].set_ylabel('√|Standardized residuals|') axes[1, 0].set_title('Scale-Location')

Residuals histogram

axes[1, 1].hist(residuals, bins=20, edgecolor='black', alpha=0.7) axes[1, 1].set_xlabel('Residuals') axes[1, 1].set_ylabel('Frequency') axes[1, 1].set_title('Histogram of Residuals')

plt.tight_layout() plt.show()

Bayesian T-Test

import pymc as pm import arviz as az import numpy as np

with pm.Model() as model: # Priors mu1 = pm.Normal('mu_group1', mu=0, sigma=10) mu2 = pm.Normal('mu_group2', mu=0, sigma=10) sigma = pm.HalfNormal('sigma', sigma=10)

# Likelihood
y1 = pm.Normal('y1', mu=mu1, sigma=sigma, observed=group_a)
y2 = pm.Normal('y2', mu=mu2, sigma=sigma, observed=group_b)

# Derived quantity
diff = pm.Deterministic('difference', mu1 - mu2)

# Sample
trace = pm.sample(2000, tune=1000, return_inferencedata=True)

Summarize

print(az.summary(trace, var_names=['difference']))

Probability that group1 > group2

prob_greater = np.mean(trace.posterior['difference'].values > 0) print(f"P(μ₁ > μ₂ | data) = {prob_greater:.3f}")

Plot posterior

az.plot_posterior(trace, var_names=['difference'], ref_val=0)

Effect Sizes

Always Calculate Effect Sizes

Effect sizes quantify magnitude, while p-values only indicate existence of an effect.

See references/effect_sizes_and_power.md for comprehensive guidance.

Quick Reference: Common Effect Sizes

Test Effect Size Small Medium Large

T-test Cohen's d 0.20 0.50 0.80

ANOVA η²_p 0.01 0.06 0.14

Correlation r 0.10 0.30 0.50

Regression R² 0.02 0.13 0.26

Chi-square Cramér's V 0.07 0.21 0.35

Important: Benchmarks are guidelines. Context matters!

Calculating Effect Sizes

Most effect sizes are automatically calculated by pingouin:

T-test returns Cohen's d

result = pg.ttest(x, y) d = result['cohen-d'].values[0]

ANOVA returns partial eta-squared

aov = pg.anova(dv='score', between='group', data=df) eta_p2 = aov['np2'].values[0]

Correlation: r is already an effect size

corr = pg.corr(x, y) r = corr['r'].values[0]

Confidence Intervals for Effect Sizes

Always report CIs to show precision:

from pingouin import compute_effsize_from_t

For t-test

d, ci = compute_effsize_from_t( t_statistic, nx=len(group1), ny=len(group2), eftype='cohen' ) print(f"d = {d:.2f}, 95% CI [{ci[0]:.2f}, {ci[1]:.2f}]")

Power Analysis

A Priori Power Analysis (Study Planning)

Determine required sample size before data collection:

from statsmodels.stats.power import ( tt_ind_solve_power, FTestAnovaPower )

T-test: What n is needed to detect d = 0.5?

n_required = tt_ind_solve_power( effect_size=0.5, alpha=0.05, power=0.80, ratio=1.0, alternative='two-sided' ) print(f"Required n per group: {n_required:.0f}")

ANOVA: What n is needed to detect f = 0.25?

anova_power = FTestAnovaPower() n_per_group = anova_power.solve_power( effect_size=0.25, ngroups=3, alpha=0.05, power=0.80 ) print(f"Required n per group: {n_per_group:.0f}")

Sensitivity Analysis (Post-Study)

Determine what effect size you could detect:

With n=50 per group, what effect could we detect?

detectable_d = tt_ind_solve_power( effect_size=None, # Solve for this nobs1=50, alpha=0.05, power=0.80, ratio=1.0, alternative='two-sided' ) print(f"Study could detect d ≥ {detectable_d:.2f}")

Note: Post-hoc power analysis (calculating power after study) is generally not recommended. Use sensitivity analysis instead.

See references/effect_sizes_and_power.md for detailed guidance.

Reporting Results

APA Style Statistical Reporting

Follow guidelines in references/reporting_standards.md .

Essential Reporting Elements

• Descriptive statistics: M, SD, n for all groups/variables

• Test statistics: Test name, statistic, df, exact p-value

• Effect sizes: With confidence intervals

• Assumption checks: Which tests were done, results, actions taken

• All planned analyses: Including non-significant findings

Example Report Templates

Independent T-Test

Group A (n = 48, M = 75.2, SD = 8.5) scored significantly higher than Group B (n = 52, M = 68.3, SD = 9.2), t(98) = 3.82, p < .001, d = 0.77, 95% CI [0.36, 1.18], two-tailed. Assumptions of normality (Shapiro-Wilk: Group A W = 0.97, p = .18; Group B W = 0.96, p = .12) and homogeneity of variance (Levene's F(1, 98) = 1.23, p = .27) were satisfied.

One-Way ANOVA

A one-way ANOVA revealed a significant main effect of treatment condition on test scores, F(2, 147) = 8.45, p < .001, η²_p = .10. Post hoc comparisons using Tukey's HSD indicated that Condition A (M = 78.2, SD = 7.3) scored significantly higher than Condition B (M = 71.5, SD = 8.1, p = .002, d = 0.87) and Condition C (M = 70.1, SD = 7.9, p < .001, d = 1.07). Conditions B and C did not differ significantly (p = .52, d = 0.18).

Multiple Regression

Multiple linear regression was conducted to predict exam scores from study hours, prior GPA, and attendance. The overall model was significant, F(3, 146) = 45.2, p < .001, R² = .48, adjusted R² = .47. Study hours (B = 1.80, SE = 0.31, β = .35, t = 5.78, p < .001, 95% CI [1.18, 2.42]) and prior GPA (B = 8.52, SE = 1.95, β = .28, t = 4.37, p < .001, 95% CI [4.66, 12.38]) were significant predictors, while attendance was not (B = 0.15, SE = 0.12, β = .08, t = 1.25, p = .21, 95% CI [-0.09, 0.39]). Multicollinearity was not a concern (all VIF < 1.5).

Bayesian Analysis

A Bayesian independent samples t-test was conducted using weakly informative priors (Normal(0, 1) for mean difference). The posterior distribution indicated that Group A scored higher than Group B (M_diff = 6.8, 95% credible interval [3.2, 10.4]). The Bayes Factor BF₁₀ = 45.3 provided very strong evidence for a difference between groups, with a 99.8% posterior probability that Group A's mean exceeded Group B's mean. Convergence diagnostics were satisfactory (all R̂ < 1.01, ESS > 1000).

Bayesian Statistics

When to Use Bayesian Methods

Consider Bayesian approaches when:

• You have prior information to incorporate

• You want direct probability statements about hypotheses

• Sample size is small or planning sequential data collection

• You need to quantify evidence for the null hypothesis

• The model is complex (hierarchical, missing data)

See references/bayesian_statistics.md for comprehensive guidance on:

• Bayes' theorem and interpretation

• Prior specification (informative, weakly informative, non-informative)

• Bayesian hypothesis testing with Bayes Factors

• Credible intervals vs. confidence intervals

• Bayesian t-tests, ANOVA, regression, and hierarchical models

• Model convergence checking and posterior predictive checks

Key Advantages

• Intuitive interpretation: "Given the data, there is a 95% probability the parameter is in this interval"

• Evidence for null: Can quantify support for no effect

• Flexible: No p-hacking concerns; can analyze data as it arrives

• Uncertainty quantification: Full posterior distribution

Resources

This skill includes comprehensive reference materials:

References Directory

• test_selection_guide.md: Decision tree for choosing appropriate statistical tests

• assumptions_and_diagnostics.md: Detailed guidance on checking and handling assumption violations

• effect_sizes_and_power.md: Calculating, interpreting, and reporting effect sizes; conducting power analyses

• bayesian_statistics.md: Complete guide to Bayesian analysis methods

• reporting_standards.md: APA-style reporting guidelines with examples

Scripts Directory

• assumption_checks.py: Automated assumption checking with visualizations

• comprehensive_assumption_check() : Complete workflow

• check_normality() : Normality testing with Q-Q plots

• check_homogeneity_of_variance() : Levene's test with box plots

• check_linearity() : Regression linearity checks

• detect_outliers() : IQR and z-score outlier detection

Best Practices

• Pre-register analyses when possible to distinguish confirmatory from exploratory

• Always check assumptions before interpreting results

• Report effect sizes with confidence intervals

• Report all planned analyses including non-significant results

• Distinguish statistical from practical significance

• Visualize data before and after analysis

• Check diagnostics for regression/ANOVA (residual plots, VIF, etc.)

• Conduct sensitivity analyses to assess robustness

• Share data and code for reproducibility

• Be transparent about violations, transformations, and decisions

Common Pitfalls to Avoid

• P-hacking: Don't test multiple ways until something is significant

• HARKing: Don't present exploratory findings as confirmatory

• Ignoring assumptions: Check them and report violations

• Confusing significance with importance: p < .05 ≠ meaningful effect

• Not reporting effect sizes: Essential for interpretation

• Cherry-picking results: Report all planned analyses

• Misinterpreting p-values: They're NOT probability that hypothesis is true

• Multiple comparisons: Correct for family-wise error when appropriate

• Ignoring missing data: Understand mechanism (MCAR, MAR, MNAR)

• Overinterpreting non-significant results: Absence of evidence ≠ evidence of absence

Getting Started Checklist

When beginning a statistical analysis:

• Define research question and hypotheses

• Determine appropriate statistical test (use test_selection_guide.md)

• Conduct power analysis to determine sample size

• Load and inspect data

• Check for missing data and outliers

• Verify assumptions using assumption_checks.py

• Run primary analysis

• Calculate effect sizes with confidence intervals

• Conduct post-hoc tests if needed (with corrections)

• Create visualizations

• Write results following reporting_standards.md

• Conduct sensitivity analyses

• Share data and code

Support and Further Reading

For questions about:

• Test selection: See references/test_selection_guide.md

• Assumptions: See references/assumptions_and_diagnostics.md

• Effect sizes: See references/effect_sizes_and_power.md

• Bayesian methods: See references/bayesian_statistics.md

• Reporting: See references/reporting_standards.md

Key textbooks:

• Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences

• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics

• Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models

• Kruschke, J. K. (2014). Doing Bayesian Data Analysis

Online resources:

• APA Style Guide: https://apastyle.apa.org/

• Statistical Consulting: Cross Validated (stats.stackexchange.com)