1. What is Normality Testing?

Normality tests assess whether a data sample comes from a normally distributed population. Many statistical tests assume normality, making this assessment crucial for proper analysis.

2. About Shapiro-Wilk and Kolmogorov-Smirnov Tests

Shapiro-Wilk Test: A powerful test for normality, especially effective with small sample sizes (n < 50).

Kolmogorov-Smirnov Test: A nonparametric test that compares the empirical distribution function with the normal distribution.

3. Interpreting p-values

• p > 0.05: Fail to reject null hypothesis (data appears normal)
• p ≤ 0.05: Reject null hypothesis (data appears non-normal)

4. Using the Calculator

Instructions: Enter numeric values separated by commas, select the test type, and click Calculate. Minimum 4 data points required.

5. Frequently Asked Questions (FAQ)

Q1: Which test should I use?
A: Shapiro-Wilk is generally preferred for small samples, while Kolmogorov-Smirnov is more versatile but less powerful.

Q2: What if my data isn't normal?
A: Consider data transformation or non-parametric statistical tests.

Q3: How many data points do I need?
A: At least 4 for Shapiro-Wilk, but more is better for reliable results.

Q4: Can I use both tests?
A: Yes, running both can provide more confidence in your assessment.

Q5: Are there visual methods to check normality?
A: Yes, Q-Q plots and histograms can complement these statistical tests.