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When researchers compare two distributions, they sometimes conduct a Kolmogorov–Smirnov "goodness of fit" test. In this...

When researchers compare two distributions, they sometimes conduct a Kolmogorov–Smirnov "goodness of fit" test. In this test they calculate a value D which they then compare to critical K values from the Kolmogorov distribution based upon stochastic Brownian processes. If such a test is done and a p value obtained, which of the following is the best description of what that p value represents?
(A) p is the probability that K > D.

(B) p is the probability that the observed sample means would be that different, due to sampling error, when the sample means are really the same.

(C) p is the probability that the D value would be that large if the only observed differences between the sample distributions are due to sampling error.

(D) p is the probability that sampling error causes the sample value of D to be outside the confidence interval for K.

(E) p is the probability that the two distributions are statistically significant.

math   Statistics-And-Probability   
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Answer #1

option C is correct

C) p is the probability that the D value would be that large if the only observed differences between the sample distributions are due to sampling error.

(since p value is probability to get the difference as or more extreme if null hypothesis is true)

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