Goodness-of-Fit Chi-Square Test: Complete Guide for Six Sigma Black Belt

Goodness-of-Fit Chi-Square Test: Complete Guide

Why Is This Important?

In the Analyze Phase of Six Sigma projects, understanding whether your data follows a specific distribution is crucial. The Goodness-of-Fit Chi-Square Test helps you:

• Validate assumptions about your data distribution
• Determine if observed data matches expected theoretical distributions
• Support decision-making for subsequent statistical analyses
• Identify when data deviates significantly from expected patterns
• Guide process improvement strategies based on data characteristics

What Is the Goodness-of-Fit Chi-Square Test?

The Goodness-of-Fit Chi-Square Test is a non-parametric statistical test that compares observed frequencies in categorical data with expected frequencies under a hypothesized distribution. It answers the question: "Does our sample data fit the assumed probability distribution?"

Key Characteristics:

• Tests categorical or grouped data
• Compares observed vs. expected frequencies
• Requires sufficiently large sample sizes (typically n ≥ 30)
• Uses the chi-square distribution
• Non-parametric (no assumptions about population parameters)

How Does It Work?

Step 1: Set Up Hypotheses

Null Hypothesis (H₀): The observed data follows the hypothesized distribution (Normal, Poisson, Uniform, Exponential, etc.)

Alternative Hypothesis (H₁): The observed data does NOT follow the hypothesized distribution

Step 2: Calculate Expected Frequencies

For each category or interval, calculate the expected frequency using the assumed distribution:

Expected Frequency = (Total Sample Size) × (Probability for that category)

Step 3: Compute the Chi-Square Test Statistic

The test statistic is calculated using the formula:

χ² = Σ [(Observed - Expected)² / Expected]

Where:

• Observed = actual frequency in each category
• Expected = theoretical frequency in each category
• Σ = sum across all categories

Step 4: Determine Degrees of Freedom

df = (Number of Categories - 1) - Number of Parameters Estimated

Examples:

• For Normal distribution: df = k - 3 (k categories, subtract 1 for constraint, 2 for mean and standard deviation)
• For Poisson: df = k - 2 (k categories, subtract 1 for constraint, 1 for λ)
• For Uniform: df = k - 1 (k categories, subtract 1 for constraint)

Step 5: Find Critical Value and Make Decision

Using chi-square distribution table with df and significance level (α, typically 0.05):

• If χ² calculated > χ² critical: Reject H₀ (data does NOT fit the distribution)
• If χ² calculated ≤ χ² critical: Fail to reject H₀ (data fits the distribution)

Practical Example

Scenario: A manufacturing process produces items. Quality data from 100 items is grouped into 5 defect categories. We want to test if the defects follow a uniform distribution.

χ² = 1.25 + 0.20 + 0.20 + 0.05 + 0.80 = 2.50

df = 5 - 1 = 4

At α = 0.05 and df = 4: χ² critical = 9.488

Decision: Since 2.50 < 9.488, fail to reject H₀. Data fits the uniform distribution.

Important Assumptions and Conditions

• Sample Size: At least 5 observations expected in each category (some texts allow 80% of categories with ≥5)
• Independence: Observations must be independent
• Categorical Data: Data should be in frequency form or grouped into categories
• Random Sample: Data should be randomly selected
• No Small Frequencies: Avoid cells with expected frequencies less than 1

Common Distributions Tested in Six Sigma

• Normal Distribution: Most common; assumes bell-shaped curve
• Poisson Distribution: For count data or rare events
• Exponential Distribution: For time-between-events data
• Uniform Distribution: When all outcomes equally likely
• Weibull Distribution: Often used in reliability engineering

Exam Tips: Answering Questions on Goodness-of-Fit Chi-Square Tests

Tip 1: Identify the Test Immediately

Look for keywords: "goodness of fit," "does data fit," "follows a distribution," "test for distributional fit." Distinguish from Chi-Square Test of Independence (which tests two categorical variables).

Tip 2: Always State Your Hypotheses Clearly

Write both H₀ and H₁ explicitly. Example:

H₀: Defect data follows a normal distribution
H₁: Defect data does not follow a normal distribution

Tip 3: Check Assumptions First

Before performing calculations, verify:

• Sample size is adequate
• Expected frequencies are sufficient (≥5 in most cells)
• Data are independent
• Data are categorical or properly grouped

Tip 4: Organize Your Calculation Table

Create a clear table with columns for:

• Category/Interval
• Observed Frequency (O)
• Expected Frequency (E)
• Difference (O - E)
• (O - E)²
• (O - E)² / E

This prevents calculation errors and shows organized thinking.

Tip 5: Calculate Expected Frequencies Accurately

For each category:

• Determine the probability for that category from the hypothesized distribution
• Multiply by total sample size
• Double-check: sum of all expected frequencies should equal total sample size

Tip 6: Don't Forget Degrees of Freedom Adjustment

The most common mistake is using df = k - 1. Remember to subtract additional degrees for parameters estimated from the sample:

• df = k - 1 - (number of parameters estimated)
• Be prepared to explain why you subtracted what you subtracted

Tip 7: Use the Correct Critical Value

When an exam gives you chi-square tables:

• Identify your significance level (α)
• Match it with the correct df row
• Write down the critical value before comparing
• State clearly: "χ² calculated = ___ vs. χ² critical = ___"

Tip 8: Make Clear Conclusions

Don't just say "reject" or "fail to reject." Connect back to the original question:

Example: "Since the calculated chi-square value of 2.50 is less than the critical value of 9.488, we fail to reject the null hypothesis. This means we have sufficient evidence at the 0.05 significance level to conclude that the defect data follows a uniform distribution."

Tip 9: Recognize When to Use Chi-Square Goodness-of-Fit vs. Other Tests

• Anderson-Darling Test: More sensitive for normality testing (preferred in practice)
• Kolmogorov-Smirnov Test: For continuous distributions with unspecified parameters
• Chi-Square GOF: Best for categorical data or when you have grouped data

Tip 10: Interpret in Context of Six Sigma

Always relate your answer to process improvement:

• If data fits normal distribution: You can use parametric tools (t-tests, ANOVA)
• If data doesn't fit: Use non-parametric methods or investigate root causes
• Discuss implications for the improvement project

Tip 11: Watch for Common Traps

• Small Expected Frequencies: If any expected frequency < 5, combine categories or note the violation
• Parameter Counting: Don't undercount parameters estimated from data
• Confusion with Chi-Square Test of Independence: That tests two categorical variables; GOF tests one variable vs. a distribution
• Rounding Errors: Be consistent with decimal places; recalculate if final sum doesn't match

Tip 12: Practice with Real Data Scenarios

Be prepared for scenarios like:

• Testing if cycle time follows exponential distribution
• Testing if defect counts follow Poisson distribution
• Testing if measurements follow normal distribution
• Testing if customer arrivals follow uniform distribution

Quick Reference: Chi-Square GOF Test Summary

Final Exam Strategy

1. Read carefully: Identify that it's a goodness-of-fit question
2. State hypotheses: Be explicit about the distribution being tested
3. Check assumptions: Verify sample size and expected frequencies
4. Organize work: Use clear tables and step-by-step calculations
5. Calculate accurately: Double-check arithmetic
6. Find critical value: Use correct df and significance level
7. Make decision: Clear comparison and conclusion
8. Interpret results: Explain what it means for the process or data

With practice and attention to these tips, you'll confidently answer any goodness-of-fit chi-square question on your Six Sigma Black Belt exam.