Quick Answer: What Is A Good KS Statistic Value?

What if P value is 0?

If the p-value, in hypothesis testing, is near 0 then the null hypothesis (H0) is rejected.


How do you perform a Kolmogorov Smirnov test?

General StepsCreate 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.More items…•

How do you interpret Ks values?

The p-value returned by the k-s test has the same interpretation as other p-values. You reject the null hypothesis that the two samples were drawn from the same distribution if the p-value is less than your significance level.

What is the Kolmogorov Smirnov test used for?

The Kolmogorov-Smirnov test (Chakravart, Laha, and Roy, 1967) is used to decide if a sample comes from a population with a specific distribution. where n(i) is the number of points less than Yi and the Yi are ordered from smallest to largest value.

What does P .05 mean?

statistically significant test resultP > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected.

What is KS statistic in logistic regression?

KS Statistic or Kolmogorov-Smirnov statistic is the maximum difference between the cumulative true positive and cumulative false positive rate. It is often used as the deciding metric to judge the efficacy of models in credit scoring.

What is a Ks value?

In statistics, the Kolmogorov–Smirnov test (K–S test or KS test) is a nonparametric test of the equality of continuous (or discontinuous, see Section 2.2), one-dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K–S test), or to compare two …

Why do we test for normality?

A normality test is used to determine whether sample data has been drawn from a normally distributed population (within some tolerance). A number of statistical tests, such as the Student’s t-test and the one-way and two-way ANOVA require a normally distributed sample population.

Why is p value important?

The P value means the probability, for a given statistical model that, when the null hypothesis is true, the statistical summary would be equal to or more extreme than the actual observed results [2]. … The smaller the P value, the greater statistical incompatibility of the data with the null hypothesis.

How do you run a Kolmogorov Smirnov test?

In order to test for normality with Kolmogorov-Smirnov test or Shapiro-Wilk test you select analyze, Descriptive Statistics and Explore. After select the dependent variable you go to graph and select normality plot with test (continue and OK).

What does a negative p value mean?

“The low p-value shows the alternative hypothesis is true.” A low p-value provides statistical evidence to reject the null hypothesis—but that doesn’t prove the truth of the alternative hypothesis.

What is KS test p value?

The KS test report the maximum difference between the two cumulative distributions, and calculates a P value from that and the sample sizes. … It tests for any violation of that null hypothesis — different medians, different variances, or different distributions.

What does P value mean?

In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. … A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.

How do you compute the p value?

The p-value is calculated using the sampling distribution of the test statistic under the null hypothesis, the sample data, and the type of test being done (lower-tailed test, upper-tailed test, or two-sided test). The p-value for: a lower-tailed test is specified by: p-value = P(TS ts | H 0 is true) = cdf(ts)

How do you know if two distributions are similar?

The Kolmogorov-Smirnov test tests whether two arbitrary distributions are the same. It can be used to compare two empirical data distributions, or to compare one empirical data distribution to any reference distribution. It’s based on comparing two cumulative distribution functions (CDFs).