

- Trending Categories
Data Structure
Networking
RDBMS
Operating System
Java
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
How to identify the difference between Kolmogorov Smirnov test and Chi Square Goodness of fit test in R?
The Chi Square Goodness of fit test is used to test whether the distribution of nominal variables is same or not as well as for other distribution matches and on the other hand the Kolmogorov Smirnov test is only used to test to the goodness of fit for a continuous data. The difference is not about the programming tool, it is a concept of statistics.
Example
> x<-rnorm(20) > x
Output
[1] 0.078716115 -0.682154062 0.655436957 -1.169616157 -0.688543382 [6] 0.646087104 0.472429834 2.277750805 0.963105637 0.414918478 [11] 0.575005958 -1.286604138 -1.026756390 2.692769261 -0.835433410 [16] 0.007544065 0.925296720 1.058978610 0.906392907 0.973050503
Example
> ks.test(x,pnorm) One-sample Kolmogorov-Smirnov test data: x D = 0.2609, p-value = 0.1089 alternative hypothesis: two-sided Chi-Square test: > chisq.test(x,p=rep(1/20,20)) Error in chisq.test(x, p = rep(1/20, 20)) : all entries of 'x' must be nonnegative and finite
With discrete distribution −
Example
> y<-rpois(200,5) > y
Output
[1] 6 8 7 3 5 7 6 5 2 6 4 4 3 6 6 6 6 11 7 5 4 8 6 1 3 [26] 10 4 4 9 5 2 6 4 1 5 4 4 5 1 7 8 7 3 6 6 6 2 8 7 6 [51] 7 5 5 4 6 5 3 5 3 4 4 9 3 3 3 8 3 3 2 5 4 6 6 8 4 [76] 6 12 6 1 5 5 5 0 7 4 7 7 3 2 5 5 2 5 5 4 6 4 3 4 4 [101] 4 6 5 1 2 4 4 4 8 5 8 6 3 4 5 2 3 3 3 6 7 4 4 5 3 [126] 5 5 5 8 2 5 8 1 2 3 5 9 4 3 5 6 3 6 3 6 3 7 4 4 1 [151] 3 5 0 7 2 9 6 10 2 6 4 5 1 7 2 8 7 4 2 5 4 2 4 5 6 [176] 3 9 3 9 5 9 7 3 1 3 9 5 6 3 10 7 5 5 6 7 4 2 5 5 1
Example
> chisq.test(y,p=rep(1/200,200)) Chi-squared test for given probabilities data: y X-squared = 203.61, df = 199, p-value = 0.3964 Warning message: In chisq.test(y, p = rep(1/200, 200)) : Chi-squared approximation may be incorrect
Example
> a<-sample(0:9,100,replace=TRUE) > a
Output
[1] 4 6 1 8 1 7 3 9 8 5 4 0 7 2 2 4 6 2 1 2 1 9 1 3 1 9 2 9 1 8 4 0 4 7 1 7 1 [38] 0 1 5 9 6 5 4 6 6 9 6 1 0 8 9 4 8 2 8 1 6 9 1 0 4 6 8 8 1 1 0 3 2 6 7 2 1 [75] 7 9 9 8 2 6 4 7 3 4 0 9 5 5 9 4 5 7 8 7 3 0 1 4 8 5
Example
> ks.test(a,pnorm) One-sample Kolmogorov-Smirnov test data: a D = 0.76134, p-value < 2.2e-16 alternative hypothesis: two-sided Warning message: In ks.test(a, pnorm) : ties should not be present for the Kolmogorov-Smirnov test > chisq.test(a,p=rep(1/100,100)) Chi-squared test for given probabilities data: a X-squared = 198.88, df = 99, p-value = 1.096e-08 Warning message: In chisq.test(a, p = rep(1/100, 100)) : Chi-squared approximation may be incorrect
- Related Questions & Answers
- How to perform chi square test for goodness of fit in R?
- How to perform one sample Kolmogorov-Smirnov test in R?
- Difference Between Test Plan and Test Strategy
- Difference between Unit Test vs. Integration Test
- How to test for the difference between two regression coefficients in R?
- Difference between Use Case and Test Case
- Difference between test () and exec () methods in Javascript
- Difference Between Acid Test Ratio and Current Ratio
- Difference between Test-Path and Resolve-Path in PowerShell?
- How to find the power of t test in R?
- How to perform fisher test in R?
- How to perform Friedman test in R?
- Test Condition Vs Test Scenario – What’s the Difference?
- Test Strategy Vs Test Plan – What’s the Difference?
- Test Case vs Test Scenario – What’s the Difference?
Advertisements