Kolmogorov-Smirnov Test  

 

    "This test can be used to test for goodness of fit.  A sample is selected from an unknown population and its goodness of fit to a hypothetical model of the population must be tested.  Both the sample and and the hypothetical model are plotted together in cumulative form, each scaled so their cumulative sums are 1.0.  We then look for the greatest difference between the two; this is the Kolmogorov-Smirnov statistic, K-S.  Table 1 gives critical values for the K-S statistic.   If the computed test statistic does not fall into the critical region then we cannot reject the null hypothesis.   Conversely, if the statistic does fall into the critical region then we can reject the null hypothesis." (from Chuck's book- need info)

 

Click here for a very useful site to perform the Kolmogorov-Smirnov test. 

 

  Table 1:   Critical values of the Kolmogorov-Smirnov Test Statistic

SAMPLE SIZE
(N)

LEVEL OF SIGNIFICANCE FOR D  =  MAXIMUM [ F0(X)  -  Sn(X) ]

.20

.15

.10

.05

.01

1

.900

.925

.950

.975

.995

2

.684

.726

.776

.842

.929

3

.565

.597

.642

.708

.828

4

.494

.525

.564

.624

.733

5

.446

.474

.510

.565

.669

6

.410

.436

.470

.521

.618

7

.381

.405

.438

.486

.577

8

.358

.381

.411

.457

.543

9

.339

.360

.388

.432

.514

10

.322

.342

.368

.410

.490

11

.307

.326

.352

.391

.468

12

.295

.313

.338

.375

.450

13

.284

.302

.325

.361

.433

14

.274

.292

.314

.349

.418

15

.266

.283

.304

.338

.404

16

.258

.274

.295

.328

.392

17

.250

.266

.286

.318

.381

18

.244

.259

.278

.309

.371

19

.237

.252

.272

.301

.363

20

.231

.246

.264

.294

.356

25

.210

.220

.240

.270

.320

30

.190

.200

.220

.240

.290

35

.180

.190

.210

.230

.270

OVER 35

        1.07
         ____
      
  N

        1.14
         ____
      
  N

        1.22
         ____
      
  N

        1.36
         ____
      
  N

        1.63
         ____
      
  N