Dataplot Vol 1 Vol 2

KOLMOGOROV SMIRNOV TWO SAMPLE

Name:
... KOLMOGOROV SMIRNOV TWO SAMPLE TEST
Type:
Analysis Command
Purpose:
Perform a Kolmogorov-Smirnov two sample test that two data samples come from the same distribution. Note that we are not specifying what that common distribution is.
Description:
The one sample Kolmogorov-Smirnov (K-S) test is based on the empirical distribution function (ECDF). Given N data points Y1, Y2, ..., YN the ECDF is defined as

$$E_{N} = \frac{n_{i}}{N}$$

where ni is the number of points less than Yi. This is a step function that increases by 1/N at the value of each data point. We can graph a plot of the empirical distribution function with a cumulative distribution function for a given distribution. The one sample K-S test is based on the maximum distance between these two curves. That is,

$$D = \max_{1 \le i \le N}|F(Y_{i}) - \frac{i} {N}|$$

where F is the theoretical cumulative distribution function.

The two sample K-S test is a variation of this. However, instead of comparing an empirical distribution function to a theoretical distribution function, we compare the two empirical distribution functions. That is,

$$D = |E_1(i) - E_2(i)|$$

where E1 and E2 are the empirical distribution functions for the two samples. Note that we compute E1 and E2 at each point in both samples (that is both E1 and E2 are computed at each point in each sample).

More formally, the Kolmogorov-Smirnov two sample test statistic can be defined as follows.

 H0: The two samples come from a common distribution. Ha: The two samples do not come from a common distribution. Test Statistic: The Kolmogorov-Smirnov two sample test statistic is defined as $$D = |E_1(i) - E_2(i)|$$ where E1 and E2 are the empirical distribution functions for the two samples. Significance Level: $$\alpha$$ Critical Region: The hypothesis regarding the distributional form is rejected if the test statistic, D, is greater than the critical value obtained from a table. There are several variations of these tables in the literature that use somewhat different scalings for the K-S test statistic and critical regions. These alternative formulations should be equivalent, but it is necessary to ensure that the test statistic is calculated in a way that is consistent with how the critical values were tabulated. Dataplot uses the critical values from Chakravart, Laha, and Roy (see Reference: below).

The quantile-quantile plot, bihistogram, and Tukey mean-difference plot are graphical alternatives to the two sample K-S test.

Syntax:
KOLMOGOROV SMIRNOV TWO SAMPLE TEST <y1> <y2>
<SUBSET/EXCEPT/FOR/qualification>
where <y1> is the first response variable;
<y2> is the second response variable;
and where the <SUBSET/EXCEPT/FOR qualification> is optional.
Examples:
KOLMOGOROV-SMIRNOV TWO SAMPLE TEST Y1 Y2
KOLMOGOROV-SMIRNOV TWO SAMPLE TEST Y1 Y2 SUBSET Y2 > 0
Note:
The KOLMOGOROV-SMIRNOV TWO SAMPLE TEST command automatically saves the following parameters.

 STATVAL - value of the K-S two sample statistic CUTUPP90 - 90% critical value (alpha = 0.10) for the K-S two sample test statistic CUTUPP95 - 95% critical value (alpha = 0.05) for the K-S two sample test statistic CUTUPP99 - 99% critical value (alpha = 0.01) for the K-S two sample test statistic

These parameters can be used in subsequent analysis.

Default:
None
Synonyms:
The word test in the command is optional. Also, TWO can be entered as 2. For example,

KOLMOGOROV SMIRNOV 2 SAMPLE Y1 Y2
Related Commands:
 KOMOGOROV SMIRNOV GOODNESS OF FIT TEST = Perform Kolmogorov-Snirnov goodness of fit test. CHI-SQUARE TWO SAMPLE TEST = Perform chi-square two sample test. BIHISTOGRAM = Generates a bihistogram. QUANTILE-QUANTILE PLOT = Generates a quantile-quantile plot. TUKEY MEAN DIFFERENCE PLOT = Generates a Tukey mean difference plot.
Reference:
Chakravart, Laha, and Roy (1967), "Handbook of Methods of Applied Statistics, Volume I," John Wiley, pp. 392-394.

Press, Teukolsky, Vetterling, and Flannery (1992), "Numerical Recipes in Fortan: The Art of Scientific Computing," Second Edition, Cambridge University Press, pp. 614-622.

Applications:
Distributional Analysis
Implementation Date:
1998/12
Program:

SKIP 25
.
DELETE Y2 SUBSET Y2 < 0
SET WRITE DECIMALS 4
KOLMOGOROV-SMIRNOPV TWO SAMPLE TEST Y1 Y2

The following output is generated.
             Kolmogorov-Smirnov Two Sample Test

First Response Variable:  Y1
Second Response Variable: Y2

H0: The Two Samples Come From the
Same (Unspecified) Distribution
Ha: The Two Samples Come From
Different Distributions

Sample One Summary Statistics:
Number of Observations:                  249
Sample Mean:                             20.1446
Sample Standard Deviation:               6.4147
Sample Minimum:                          9.0000
Sample Maximum:                          39.0000

Sample Two Summary Statistics:
Number of Observations:                  79
Sample Mean:                             30.4810
Sample Standard Deviation:               6.1077
Sample Minimum:                          18.0000
Sample Maximum:                          47.0000

Test Statistic Value:                    0.6003

Conclusions (Upper 1-Tailed Test)

------------------------------------------------------------------------
Null
Null   Significance           Test       Critical     Hypothesis
Hypothesis          Level      Statistic    Region (>=)     Conclusion
------------------------------------------------------------------------
Same           90.0%         0.6003         0.1575         REJECT
Same           95.0%         0.6003         0.1756         REJECT
Same           99.0%         0.6003         0.2105         REJECT


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Date created: 6/5/2001
Last updated: 10/30/2015