Statistical Assumption Checker & Diagnostic Guide

Pro v1.1.0 1 view

Educational guide for graduate statistics students learning to test and interpret parametric test assumptions including normality, homogeneity of variance, linearity, independence, and outliers with clear explanations.

What You Get

Combines automated assumption testing with educational explanations that help graduate students understand why assumptions matter, how to interpret diagnostic results, and what to do when assumptions are violated.

The Problem

Graduate statistics students struggle with assumption testing because most resources either provide theory without application or automation without understanding. Students need to learn both the mechanics (how to run tests) and the reasoning (why assumptions matter, how to interpret results, when violations are acceptable). Without proper guidance, students either skip assumption checking entirely or misinterpret diagnostic results, leading to invalid statistical conclusions in their thesis work.

The Solution

An educational workflow that teaches assumption checking through doing. For each assumption (normality, homogeneity of variance, linearity, independence, outliers), students get: (1) clear explanation of why the assumption matters for valid inference, (2) multiple diagnostic approaches (visual, statistical, descriptive) with interpretation guidance, (3) sample size considerations explained, (4) decision frameworks for what to do when assumptions are violated. Includes complete Python code with extensive comments explaining the statistical reasoning, interpretation sections that teach how to read diagnostic plots, and specific recommendations for alternative approaches when assumptions fail. Emphasizes visual inspection over blind reliance on p-values and builds statistical intuition through triangulation of multiple diagnostic approaches.

How It Works

1 Understand analysis context by identifying research question, variables, sample size, and planned statistical test
2 Handle missing data first by assessing amount and pattern, then choosing appropriate strategy (deletion, imputation)
3 Check normality using Shapiro-Wilk test, Q-Q plots (most important), histograms, skewness, and kurtosis with sample size considerations
4 Check homogeneity of variance using Levene's test, variance ratios, and box plots with interpretation guidance
5 Check linearity (regression/correlation) using scatterplots with regression lines and residual plots
6 Check independence by examining study design and using Durbin-Watson test or ACF plots when applicable
7 Detect outliers using z-score and IQR methods with guidance on investigation before removal
8 Synthesize all results and decide on appropriate statistical approach with specific recommendations for violations

What You'll Need

Python 3.x with scipy, statsmodels, pandas, numpy, matplotlib, seaborn, sklearn
Dataset in CSV format with clear variable names
Understanding of research design and planned statistical analysis
Willingness to learn statistical concepts through guided practice

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Examples

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Independent T-Test Assumption Checking

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