- This MATLAB function returns a test decision for the null hypothesis that the data in vectors x1 and x2 are from the same continuous distribution, using the two-sample Kolmogorov-Smirnov test
- Kolmogorov - Smirnov test of the goodness-of-fit version 1.0.1 (2.13 KB) by Maria M. Estimates p-value for the goodness of a given fit using Kolmogorov - Smirnov statistics
- e the normality of each column of a data matrix prior to perfor
- 2. I'm using MATLAB to analyze some neuroscience data, and I made an interspike interval distribution and fit an exponential to it. Then, I wanted to check this fit using a Kolmogorov-Smirnov test with MATLAB. The data for the neuron spikes is just stored in a vector of spikes. The spikes vector is a 111 by 1 vector, where each entry is another.
- Test-stat for Kolmogorov-Smirnov test. Learn more about kolmogorov-smirnov

- 1. I observed a strange behavior of the 2 sample Kolmogorov - Smirnov test in Matlab and I'm not sure if I missing the obvious or if there is indeed an issue with implementation of the test? Below is a code example. I estimate the power of the test (counting how often H_0 is rejected) as a function of the number of observations (from 10 to 50)
- Seems like Matlab has these tables built in the 'kstest' but the distribution of Dn is not available as a separate function. When n is large then we can use KS distribution to ﬁnd c since = P(Dn ≈ c|H0) 1 − H(c). and we can use the table for H to ﬁnd c. KS test for two samples. Kolmogorov-Smirnov test for two samples is very similar
- The Kolmogorov-Smirnov test is often to test the normality assumption required by many statistical tests such as ANOVA, the t-test and many others. However, it is almost routinely overlooked that such tests are robust against a violation of this assumption if sample sizes are reasonable, say N ≥ 25. *
- 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 samples (two-sample K-S test)

- g ks.test function in R. Definition of a cumulative distribution.
- This video will guide you on how to solve test of normality (Kolmogorov-Smirnov) by using SPSS.for more information...click here : http://statisticisfun.blo..
- kstest.
**Kolmogorov-Smirnov****test**of the distribution of one sample. Syntax. H = kstest(X) H = kstest(X,cdf) H = kstest(X,cdf,alpha,tail) [H,P,KSSTAT,CV] = kstest(X,cdf,alpha,tail) Description. H = kstest(X) performs a**Kolmogorov-Smirnov****test**to compare the values in the data vector X with a standard normal distribution (that is, a normal distribution having mean 0 and variance 1) - One-sample Kolmogorov-Smirnov test: the data in vector x comes from a standard normal distribution (mean 0, std 1). Lilliefors test: the data in vector x comes from a distribution in the normal family. Anderson-Darling test: the data in vector x is from a population with a normal distribution
- dist can be any string for which a function distcdf that calculates the CDF of distribution dist exists.. With the optional argument string alt, the alternative of interest can be selected.If alt is != or <>, the null is tested against the two-sided alternative F != G.In this case, the test statistic ks follows a two-sided Kolmogorov-Smirnov distribution
- Kolmogorov-Smirnov Test Example: We generated 1,000 random numbers for normal, double exponential, t with 3 degrees of freedom, and lognormal distributions. In all cases, the Kolmogorov-Smirnov test was applied to test for a normal distribution

Esta función de MATLAB devuelve una decisión de prueba para la hipótesis nula de que los datos en vectores y son de la misma distribución continua, utilizando el archivo .x1x2prueba Kolmogorov-Smirnov de dos muestras La hipótesis alternativa es que y son de diferentes distribuciones continuas.x1x2 El resultado es si la prueba rechaza la hipótesis nula en el nivel de significancia del 5%. One-sample Kolmogorov-Smirnov test: the data in vector x comes from a standard normal distribution (mean 0, std 1). Lilliefors test: the data in vector x comes from a distribution in the normal family. Anderson-Darling test: the data in vector x is from a population with a normal distribution. If the null hypothesis is rejected (an outcome of 1.

The two-sample Kolmogorov-Smirnov test is used to test whether two samples come from the same distribution. The procedure is very similar to the One Kolmogorov-Smirnov Test (see also Kolmogorov-Smirnov Test for Normality).. Suppose that the first sample has size m with an observed cumulative distribution function of F(x) and that the second sample has size n with an observed cumulative. The Test Statistic¶. The Kolmogorov-Smirnov test is constructed as a statistical hypothesis test. We determine a null hypothesis, , that the two samples we are testing come from the same distribution.Then we search for evidence that this hypothesis should be rejected and express this in terms of a probability 1-표본 콜모고로프-스미르노프(Kolmogorov-Smirnov) The Kolmogorov-Smirnov Test for Goodness of Fit. Journal of the American Statistical Association. Vol. 46, No. 253, 1951, pp. 68-78. 다음 MATLAB 명령에 해당하는 링크를 클릭했습니다 Kolmogorov-Smirnov test for inverse Gaussian... Learn more about statistics, probabilit

I want to do Kolmogorov -Smirnov test to see whether my data follows a particular distribution or not? When I fitted my data to lognormal distribution it fitted well. But When I am doing ks test, it is rejecting the null hypothesis. Also, my sample size is very large like 1047304 samples Esta función de MATLAB devuelve una decisión de prueba para la hipótesis nula de que los datos en vector proceden de una distribución normal estándar, con la alternativa de que no procede de dicha distribución, utilizando el archivo .xprueba Kolmogorov-Smirnov de una muestra El resultado es si la prueba rechaza la hipótesis nula en el nivel de significancia del 5%, o de otra manera.h1 * The Kolmogorov-Smirnov test (KS Test) is a bit more complex and allows you to detect patterns you can't detect with a Student's T-Test*. From Wikipedia: The Kolmogorov-Smirnov statistic quantifies a distance between the empirical distribution function of the sample and the cumulative distribution function of the reference distribution, or between the empirical distribution functions.

This MATLAB function returns a test decision for the null hypothesis that the data in vector x comes from a standard normal distribution, against the alternative that it does not come from such a distribution, using the one-sample Kolmogorov-Smirnov test How do I use the Kolmogorov-Smirnov test on my... Learn more about kstest, kolmogorov-smirnov, f-distribution, chi

- The Matlab results agree with the SPSS 18 results andhence-not with the newer results.The Kolmogorov-Smirnov test is often to test the normality assumption required by many statistical tests such as ANOVA, the t-test and many others
- Figure 3 - Kolmogorov-Smirnov test for Example 1. Columns A and B contain the data from the original frequency table. but my problem is that, as I am going to use MATLAB or EXCEL softwares for this purpose, I do not know how I can use these softwares for this test
- Kolmogorov-Smirnov Test (one or two sampled test verifies the equality of distributions) is implemented in many software programs: Mathematica has KolmogorovSmirnovTest MATLAB has kstest in its Statistics Toolbox. R 's statistics base-package implements the test as ks.test {stats} in its stats.
- To formally test a dataset for normality, i.e., to perform a one-sided Kolmogorov-Smirnov test in MATLAB, use the following command: h = kstest(x) where x is a column of data containing all datapoints. The null hypothesis assumes that x is distributed normally (i.e., that there is no difference between x and a normal distribution)

Kruskal-Wallis test. Tests if multiple samples are all drawn from the same populations (or equivalently, from different populations with the same distribution), against the alternative that they are not all drawn from the same population. kstest: One-sample Kolmogorov-Smirnov test Kolmogorov-Smirnov Test Summary The Kolmogorov-Smirnov test (KS-test) tries to determine if two datasets differ significantly. The KS-test has the advantage of making no assumption about the distribution of data. (Technically speaking it is non-parametric and distribution free. Three complementary methods are available for comparing models: - by P-value: this is the probability that the tested hypothesis H0 (the model fits the datapoints) is wrongly rejected.This is obtained by applying the modified Kolmogorov-Smirnov test. The AVadequat value is its complement, 1 − P-value;. by the difference between the theoretical and empirical survival functions: MGRAPH is. gtest 2-way log-likelihood contingency table test histplot Matrix of histograms homosub Homogeneous subsets from a symmetric binary matrix of signif diffs kruskwal Randomized Kruskal-Wallace (& Mann-Whitney) test for 2+ samples ksprob Significance level for Kolmogorov-Smirnov test kstest1 1-sample randomized Kolmogorov-Smirnov test

The Kolmogorov-Smirnov test ( KS-test) is one of the useful and general nonparametric method for comparing two samples. It can be used to test whether the two samples are different in the location and the shape of empirical distribution functions. As a nonparametric test, it does not require the normality of the population Critical Values for the Two-sample Kolmogorov-Smirnov test (2-sided) Table gives critical D -values for α = 0.05 (upper value) and α = 0.01 (lower value) for various sample sizes Statistics - Kolmogorov Smirnov Test - This test is used in situations where a comparison has to be made between an observed sample distribution and theoretical distribution

Kolmogorov-Smirnov statistic: 对于一个样本集的累计分布函数 F n (x) F n ( x ) 和一个假设的理论分布 F (x) F ( x ) ,Kolmogorov-Smirnov statistic定义为： s u p x s u p x 是距离的上确界(supremum)， 基于Glivenko-Cantelli theorem，若 X i X i 服从理论分布 F (x) F ( x ) ，则当n趋于无穷时 D n D n 趋于0 常用的有Shapiro-Wilk检验、Kolmogorov-Smirnov检验、Anderson-Darling检验等，可以得到一个p值用于定量描述正态性。 Shapiro-Wilk Test. 内涵理解：Shapiro-Wilk test； Shapiro-Wilk正态分布检验. Kolmogorov-Smirnov test. Matlab函数：kstest. Anderson-Darling Test. 内涵理解：Anderson-Darling Normality Test The Kolmogorov-Smirnov test is used to test whether or not or not a sample comes from a certain distribution.. To perform a one-sample or two-sample Kolmogorov-Smirnov test in R we can use the ks.test() function.. This tutorial shows example of how to use this function in practice

Computes the p-value, or observed significance level, of a two-sample Kolmogorov-Smirnov test evaluating the null hypothesis that x and y are samples drawn from the same probability distribution. Specifically, what is returned is an estimate of the probability that the kolmogorovSmirnovStatistic(double[], double[]) associated with a randomly selected partition of the combined sample into. The technique used in this article is from Facchinetti (2009), A Procedure to Find Exact Critical Values of Kolmogorov-Smirnov Test. An overview of the Facchinetti method. Suppose you have a sample of size n and you perform a Kolmogorov-Smirnov test that compares the empirical distribution to a reference distribution You can use simulation to estimate the critical value for the Kolmogorov-Smirnov statistical test for normality, which is sometimes abbreviated as the KS test. For the data in my previous article, the null hypothesis is that the sample data follow a N(59, 5) distribution. The alternative hypothesis is that they do not

In this guide, you will learn how to produce a one-sample and two-sample Kolmogorov-Smirnov (K-S) test in IBM® SPSS® Statistics software (SPSS) using a practical example to illustrate the process. You will find links to the example dataset, and you are encouraged to replicate this example Kolmogorov-Smirnov test is distribution free in the sense that if . H. 0. is true, the significance level does not depend on . F . and G [5]. The generalization of the classical Kolmogorov-Smirnov test is appropriate to analyse random samples defined [10]. The in two or three dimensions Kolmogorov-Smirnov test for two independent samples was. The tests I'm currently using to test the goodness of fit include Kolmogorov-Smirnov, Anderson-Darling and chi-squared. The probability density function and QQ plot for a data set (n=24) is. The Kolmogorov-Smirnov test is a nonparametric test that tries to find out if the two data distributions differ significantly. In some cases, it outperforms other tests such as t-test as it does not make any assumption about the underlying data distribution

minimize Kolmogorov-Smirnov distance. Follow 19 views (last 30 days) Show older comments. gaia buratti on 24 Jul 2011. Vote. 0. ⋮ . Vote. 0. Hi, I have 5 parameters to estimate (m,V,M,r,F). Find the treasures in MATLAB Central and discover how the community can help you! Start Hunting ** 8 Kolmogorov-Smirnov Test of U(0,1) •For uniform random numbers between 0 and 1 —expected CDF Fe(x) = x •If x > j-1+observations in a sample of n observations —observed CDF Fo(x) = j/n •To test whether a sample1of n random numbers is from U(0,1) —sort n observations in increasing order —let the sorted numbers be {x1, x2, , xn}, xn-1≤ xn •Compare resulting K+, K-values with**.

How to apply one sample Kolmogorov-Smirnov... Learn more about kolmogorov-smirnov, one smaple kolmogorov-smirnov test % KSTEST2 Two-sample Kolmogorov-Smirnov goodness-of-fit hypothesis test. % H = KSTEST2(X1,X2,ALPHA,TYPE) performs a Kolmogorov-Smirnov (K-S) test % to determine if independent random samples, X1 and X2, are drawn from % the same underlying continuous population. ALPHA and TYPE are optiona Request PDF | Kolmogorov Smirnov Test for Generalized Pareto Distribution | The Kolmogorov- Smirnov Statistic is considered for testing the goodness of fit of the three parameter Generalized. En statistique , le test de Kolmogorov - Smirnov ( test K - S ou test KS ) est un test non paramétrique de l'égalité des distributions de probabilité continues (ou discontinues, voir section 2.2 ), unidimensionnelles qui peuvent être utilisées pour comparer un échantillon avec un distribution de probabilité de référence (test K - S à un échantillon), ou pour comparer deux. In this case, the % test statistic @var{ks} has a one-sided Kolmogorov-Smirnov % distribution. The default is the two-sided case. % % The p-value of the test is returned in @var{pval}

h = adtest(x) returns a test decision for the null hypothesis that the data in vector x is from a population with a normal distribution, using the Anderson-Darling test.The alternative hypothesis is that x is not from a population with a normal distribution. The result h is 1 if the test rejects the null hypothesis at the 5% significance level, or 0 otherwise In KScorrect: Lilliefors-Corrected Kolmogorov-Smirnov Goodness-of-Fit Tests. Description Usage Arguments Details Value Author(s) See Also Examples. Description. Density, distribution function, quantile function, and random generation for a univariate (one-dimensional) distribution composed of a mixture of normal distributions with means equal to mean, standard deviations equal to sd, and. The Kolmogorov-Smirnov test is used to test whether or not or not a sample comes from a certain distribution.. To perform a Kolmogorov-Smirnov test in Python we can use the scipy.stats.kstest() for a one-sample test or scipy.stats.ks_2samp() for a two-sample test.. This tutorial shows an example of how to use each function in practice. Example 1: One Sample Kolmogorov-Smirnov Test * Kolmogorov-Smirnov test, which uses the maximum absolute difference between the distribution functions of the samples*. This is in general an attractive test because it is distribution-free, it makes use of each individual data point in the samples, and it is independent of direction of ordering of the data

Kolmogorov-Smirnov Distribution Richard Simard Universit e de Montr eal Pierre L'Ecuyer Universit e de Montr eal Abstract We propose an algorithm to compute the cumulative distribution function of the two-sided Kolmogorov-Smirnov test statistic D n and its complementary distribution in a fast and reliable way Many parametric tests require normally distributed variables. The one-sample Kolmogorov-Smirnov test can be used to test that a variable (for example, income) is normally distributed. Statistics. Mean, standard deviation, minimum, maximum, number of nonmissing cases, and quartiles. One-Sample Kolmogorov-Smirnov Test Data Considerations. Data The kolmogorov smirnov test 1. The Kolmogorov-Smirnov Test XIMB 2. The Kolmogorov-Smirnov Test (K-S Test) is used to test the goodnessof-fit of a theoretical frequency distribution, i.e., whether there is a significant difference between an observed frequency distribution and a given theoretical (expected) frequency distribution. •Similar to what the Chi-Square test does, but the K-S test.

The Kolmogorov - Smirnov test assumes that the data came from a continuous distribution. The Kolmogorov - Smirnov test effectively uses a test statistic based on where is the empirical CDF of data and is the CDF of dist. For multivariate tests, the sum of the univariate marginal -values is used and is assumed to follow a. Power-law Distributions in Empirical Data. This page is a companion for the SIAM Review paper on power-law distributions in empirical data, written by Aaron Clauset (me), Cosma R. Shalizi and M.E.J. Newman. This page hosts implementations of the methods we describe in the article, including several by authors other than us

The Lilliefors test is a two-sided goodness-of-fit test suitable when the parameters of the null distribution are unknown and must be estimated. This is in contrast to the one-sample Kolmogorov-Smirnov test, which requires the null distribution to be completely specified. The Lilliefors test statistic is In addition, the normality test is used to find out that the data taken comes from a population with normal distribution. The test used to test normality is the Kolmogorov-Smirnov test. Based on this sample the null hypothesis will be tested that the sample originates from a normally distributed population against the rival hypothesis that the population is abnormally distributed Details. If y is numeric, a two-sample test of the null hypothesis that x and y were drawn from the same continuous distribution is performed.. Alternatively, y can be a character string naming a continuous (cumulative) distribution function, or such a function. In this case, a one-sample test is carried out of the null that the distribution function which generated x is distribution y with. Supporting Information. To open the Two-Sample Kolmogorov-Smirnov dialog box: With the worksheet active, click Statistics: Nonparametric Tests: Two Sample Kolmogorov-Smirnov Test...; See Also: Introduction: Two-Sample Kolmogorov-Smirnov Test

Takes a sample size and a confidence level and computes the corresponding critical value basing on the kolmogorov-smirnov test ks.test.critical.value: Critical Value for Kolmogorov-Smirnov Test in BoutrosLab.plotting.general: Functions to Create Publication-Quality Plot This Kolmogorov-Smirnov test calculator allows you to make a determination as to whether a distribution - usually a sample distribution - matches the characteristics of a normal distribution. This is important to know if you intend to use a parametric statistical test to analyse data, because these normally work on the assumption that data is normally distributed * kolmogorov_smirnov_test_2 vs*. kstest2. Hi, I have a problem using/understanding Octave's Kolmogorov-Smirnov Test implementation. I am comparing it with Matlab's kstest2. For the calculation of..

- e whether we can accept or reject the hypothesis that the data is from the specified distribution at the specified level of significance. This method is not a means of comparing distributions.
- test. The LF test as a correction of the Kolmogorov-Smirnov test should not be confused with the original Kolmogorov-Smirnov test. This study adopts the algorithm used by the statistical software SPSS to calculate the p-value of the LF test, which is based on the use of the critical value table and formulation of Dellal and Wilkinson
- Implementation of the Kolmogorov-Smirnov statistical test as a Rust library. Read an introduction about this project, Rust, and the Kolmogorov-Smirnov test here. Getting Started. The Kolmogorov-Smirnov library is available as a crate, so it is easy to incorporate into your programs. Add the dependency to your Cargo.toml file
- KS-test Data Entry Use the below form to enter your data for a Kolmogorov-Smirnov test. The KS-test seeks differences between your two datasets; it is non-parametric and distribution free. Reject the null hypothesis of no difference between your datasets if P is small
- In scenario 1, the Ryan-Joiner test was a clear winner. The simulation results are below. In scenario 2, the Anderson-Darling test was the best. The simulation results are below. In scenario 3, there was not much difference between the AD and RJ test. Both were more effective at detecting Non-Normality than the Kolmogorov-Smirnov test
- Kolmogorov-Smirnov normality test This test compares the ECDF (empirical cumulative distribution function) of your sample data with the distribution expected if the data were normal. If this observed difference is adequately large, the test will reject the null hypothesis of population normality

The Kolmogorov-Smirnov (K-S) tests based on the assumptions of determined observations in the sample have been popularly applied for the analysis of the data. The existing K-S tests for one sample and two samples cannot be applied when the data contains neutrosophic observations measured from the complex system or under uncertainty. In this paper, we propose the generalization of the. * I am using Kolmogorov-Smirnov Test to check the model performance*. I have built 3 models and the accuracy is around 70%- 76% for the models (the higher the better) Chronux Analysis Software. Chronux is an open-source software package for the analysis of neural data. It was originally developed through a collaborative research effort based at the Mitra Lab in Cold Spring Harbor Laboratory.Chronux routines may be employed in the analysis of both point process and continuous data, ranging from preprocessing, exploratory and confirmatory analysis

Hi Govinda, yes given that your sample size is 300, the Kolmogorov-Smirnov test would be most appropriate. If the p value is >0.05 then you can reject the null hypothesis,. How to test normality with the Kolmogorov-Smirnov Using SPSS | Data normality test is the first step that must be done before the data is processed based on the models of research, especially if the purpose of the research is inferential. Normality test is intended to determine the distribution of the data in the variable that will be used in research Kolmogorov-Smirnov tests have the advantages that (a) the distribution of statistic does not depend on cumulative distribution function being tested and (b) the test is exact. They have the disadvantage that they are more sensitive to deviations near the centre of the distribution than at the tails We wish to use the two-sample Kolmogorov -Smirnov test to determine if there are any differences in the distribution of x for these two groups. The first line t ests the hypothesis that x for group 1 contains smaller values than for group 2 MATLAB® is used for a wide range of applications in geosciences, such as image processing in remote sensing, the generation and processing of digital elevation models, and the analysis of time series

The Kolmogorov-Smirnov test is a nonparametric test that compares the distributions of two unmatched groups.. Are the values independent? The results of a Kolmogorov-Smirnov test only make sense when the scatter is random - that whatever factor caused a value to be too high or too low affects only that one value Additional Key Words and Phrases: Hypothesis tests, Kolmogorov-Smirnov statistical test, power, data transformations. ACM Reference Format: Song-Hee Kim and Ward Whitt, 2013. The Power of Alternative Kolmogorov-Smirnov Tests Based on Trans-formations of the Data. ACM Trans. Model. Comput. Simul. V, N, Article A (January YYYY), 18 pages

Kolmogorov-Smirnov Test in R With Data From the Opinions and Lifestyle Survey (Well-Being Module) (2015) How-to Guide for R Introduction In this guide, you will learn how to produce a one-sample and two-sample Kolmogorov-Smirnov (K-S) test in R using a practical example to illustrate the process * Key facts about the Kolmogorov-Smirnov test • The two sample Kolmogorov-Smirnov test is a nonparametric test that compares the cumulative distributions of two data sets(1,2)*. • The test is nonparametric. It does not assume that data are sampled from Gaussian distributions (or any other defined distributions) The Kolmogorov-Smirnov (K-S) test is one of the most useful and general nonparametric methods for comparing two samples. It is sensitive to all types of differences between two populations (shift, shape, etc.). In this paper, we will present a thorough investigation into the K-S test including: discussion of th I would like to compute one sided kolmogorov-smirnov test using the PROC NPAR1WAY procedure. Specifically i want to test 2 distributions of real vs. estimated values, but i am not able to distinguish what distribution corresponds to F1 and which corresponds to F2 and i don't understand which criteria SAS uses to determine which values are F1 and F2 In this working code, as expected, all the Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling tests do not reject the null. If GAMMA is unavailable, then what I found instead is an indirect two-sample test using NPAR1WAY—generate some random numbers from the target distribution, and test if the above simulated distribution and the generated distribution just before are.

Find answers to Performing Statistical analysis (kolmogorov smirnov test/related) using R or Matlab or Excel from the expert community at Experts Exchang Kolmogorov-Smirnov test is used to find out the uniformity between the random numbers in a sequece. It can only be used to the pseudo random numbers. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising

Two-sample Kolmogorov-Smirnov (KS) test (Massey, 1951) can be used to compare the distributions of the observations from the two datasets.The null hypothesis (H o) is that the two dataset values are from the same continuous distribution.The alternative hypothesis (H a) is that these two datasets are from different continuous distributions. The hypothesis test can be carried out at a specific. Perform a one- or two-sample Kolmogorov-Smirnov test. Source. The two-sided one-sample distribution comes via Marsaglia, Tsang and Wang (2003).. References. Z. W. Birnbaum and Fred H. Tingey (1951) Further, there are two versions of the Kolmogorov Smirnov Sample Test that are available and we need to choose the one that suits our requirement. Although we will be implementing the KS-2 Test in Python in this post, it also makes sense to talk about KS-1 Test so that we are familiar with the conceptual differences between the two tests This is probably the most basic and widely used of the non-parametric statistical tests. Developed in the 1930s by Andrei Nikolaevich Kolmogorov and Nikolai Vasilyevich Smirnov, test allows the comparison of a frequency distribution to some other known (continuous one-dimensional) distribution, such as a Gaussian normal distribution.. Some people use the K-S test as an alternative to the. The Lilliefors (Kolmogorov-Smirnov) test is an EDF omnibus test for the composite hypothesis of normality. The test statistic is the maximal absolute difference between empirical and hypothetical cumulative distribution function. It may be computed as D=\max\{D^{+}, D^{-}\} wit The Kolmogorov-Smirnov two-sample test (K-S two sample test) is a goodness-of-fit test which is used to determine whether two underlying one-dimensional probability distributions differ. In order to find the statistic pivot of a K-S two-sample test, we calculate the cumulative function by means of empirical distribution function