Assumption of Normality > Kolmogorov-Smirnov Test

**Contents:**

- What is the Kolmogorov-Smirnov Test?
- How to run the test by hand
- Using software
- K-S Test P-Value Table
- Advantages and Disadvantages

## What is the Kolmogorov-Smirnov Test?

The Kolmogorov-Smirnov Goodness of Fit Test (K-S test) **compares your data with a known distribution and lets you know if they have the same distribution.** Although the test is nonparametric — it doesn’t assume any particular underlying distribution — it is commonly used as a test for normality to see if your data is normally distributed.It’s also used to check the assumption of normality in Analysis of Variance.

More specifically, the test compares a known hypothetical probability distribution (e.g. the normal distribution) to the distribution generated by your data — the empirical distribution function.

Lilliefors test, a corrected version of the

K-S test for normality, generally gives a more accurate approximation of the test statistic’s distribution. In fact, many statistical packages (like SPSS) combine the two tests as a “Lilliefors corrected” K-S test.

**Note: **If you’ve never compared an experimental distribution to a hypothetical distribution before, you may want to read the empirical distribution article first. It’s a short article, and includes an example where you compare two data sets simply— using a scatter plot instead of a hypothesis test.

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## How to run the test by hand

The **hypotheses **for the test are:

**Null hypothesis**(H_{0}): the data comes from the specified distribution.**Alternate Hypothesis**(H_{1}): at least one value does not match the specified distribution.

That is,

H_{0}: P = P_{0}, H_{1}: P ≠ P_{0}.

Where P is the distribution of your sample (i.e. the EDF) and P_{0} is a specified distribution.

## General Steps

The **general steps to run the test **are:

- Create an EDF for your sample data (see
*Empirical Distribution Function*for steps), - Specify a parent distribution (i.e. one that you want to compare your EDF to),
- Graph the two distributions together.
- Measure the greatest vertical distance between the two graphs.
- Calculate the test statistic.
- Find the critical value in the KS table.
- Compare to the critical value.

## Calculating the Test Statistic

The K-S test statistic measures the largest distance between the EDF F_{data}(x) and the theoretical function F_{0}(x), measured in a vertical direction (Kolmogorov as cited in Stephens 1992). The **test statistic **is given by:

Where (for a two-tailed test):

- F
_{0}(x) = the cdf of the hypothesized distribution, - F
_{data}(x) = the*empirical distribution function*of your observed data.

For one-tailed test, omit the absolute values from the formula.

If D is greater than the critical value, the null hypothesis is rejected. Critical values for D are found in the K-S Test P-Value Table.

## Example

Step 1: **Find the EDF.** In the EDF article, I generated an EDF using Excel that I’ll use for this example.

Step 2: **Specify the parent distribution. **In the same article, I also calculated the corresponding values for the gamma function.

Step 3: **Graph the functions together.** A snapshot of the scatter graph looked like this:

Step 4: **Measure the greatest vertical distance.** Let’s assume that I graphed the entire sample and the largest vertical distance separating my two graphs is .04 (in the yellow highlighted box).

Step 5: **Look up the critical value in the K-S table value.** I have 50 observations in my sample. At an alpha level of .05, the K-S table value is .190.

Step 6: Compare the results from Step 4 and Step 5. Since .04 is less than .190, the null hypothesis (that the distributions are the same) is accepted.

## Using Technology

Most **software packages** can run this test.

The R function ecdf creates empirical distribution functions. An R function *p* followed by a distribution name (pnorm, pbinom, etc.) gives a theoretical distribution function.

There are several online calculators available, like this one, and this one.

As a result of using software to test for normality, small p-values in your output generally indicate the data is not from a normal distribution (Ruppert, 2004).

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## K-S Test P-Value Table

## Advantages and Disadvantages

Advantages include:

- The test is
**distribution free**. That means you don’t have to know the underlying population distribution for your data before running this test. - The
**D statistic**(not to be confused with*Cohen’s D*) used for the test is easy to calculate. - It can be used as a
**goodness of fit test**following regression analysis. - There are
**no restrictions**on**sample size**; Small samples are acceptable. **Tables**are readily available.

Although the K-S test has many advantages, it also has a few **limitations**:

- In order for the test to work, you must specify the location, scale, and shape parameters.
**If these parameters are estimated from the data, it invalidates the test**. If you don’t know these parameters, you may want to run a less formal test (like the one outlined in the*empirical distribution function*article). - It generally
**can’t be used for discrete distributions,**especially if you are using software (most software packages don’t have the necessary extensions for discrete K-S Test and the manual calculations are convoluted). **Sensitivity**is higher at the center of the distribution and lower at the tails.

## Related Articles

## References

Chakravarti, Laha, and Roy, (1967). Handbook of Methods of Applied Statistics, Volume I, John Wiley and Sons, pp. 392-394.

Ruppert, D. (2004). Statistics and Finance: An Introduction. Springer Science and Business Media.

Stephens M.A. (1992) Introduction to Kolmogorov (1933) On the Empirical Determination of a Distribution. In: Kotz S., Johnson N.L. (eds) Breakthroughs in Statistics. Springer Series in Statistics (Perspectives in Statistics). Springer, New York, NY

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