This test is used in situations where a comparison has to be made between an observed sample distribution and theoretical distribution.

__K-S One Sample Test__

__K-S One Sample Test__

This test is used as a test of goodness of fit and is ideal when the size of the sample is small. It compares the cumulative distribution function for a variable with a specified distribution. The null hypothesis assumes no difference between the observed and theoretical distribution and the value of test statistic ‘D’ is calculated as:

**Formula**

D=Maximum|Fo(X)−Fr(X)|D=Maximum|Fo(X)−Fr(X)|

Where −

· Fo(X)Fo(X) = Observed cumulative frequency distribution of a random sample of n observations.

· and Fo(X)=knFo(X)=kn = (No.of observations ≤ X)/(Total no.of observations).

· Fr(X)Fr(X) = The theoretical frequency distribution.

The critical value of DD is found from the K-S table values for one sample test.

**Acceptance Criteria:** If calculated value is less than critical value accept null hypothesis.

**Rejection Criteria:** If calculated value is greater than table value reject null hypothesis.

**Example**

**Problem Statement:**

In a study done from various streams of a college 60 students, with equal number of students drawn from each stream, are we interviewed and their intention to join the Drama Club of college was noted.

| B.Sc. | B.A. | B.Com | M.A. | M.Com |

No. in each class | 5 | 9 | 11 | 16 | 19 |

It was expected that 12 students from each class would join the Drama Club. Using the K-S test to find if there is any difference among student classes with regard to their intention of joining the Drama Club.

**Solution:**

HoHo: There is no difference among students of different streams with respect to their intention of joining the drama club.

We develop the cumulative frequencies for observed and theoretical distributions.

Streams | No. of students interested in joining | FO(X)FO(X) | FT(X)FT(X) | |FO(X)−FT(X)||FO(X)−FT(X)| | |

| Observed(O) | Theoretical(T) | | | |

B.Sc. | 5 | 12 | 5/60 | 12/60 | 7/60 |

B.A. | 9 | 12 | 14/60 | 24/60 | 10/60 |

B.COM. | 11 | 12 | 25/60 | 36/60 | 11/60 |

M.A. | 16 | 12 | 41/60 | 48/60 | 7/60 |

M.COM. | 19 | 12 | 60/40 | 60/60 | 60/60 |

Total | n=60 | ||||

Test statistic |D||D| is calculated as:

D=Maximum|F0(X)−FT(X)|=1160=0.183D=Maximum|F0(X)−FT(X)|=1160=0.183

The table value of D at 5% significance level is given by

D0.05=1.36n√=1.3660√=0.175D0.05=1.36n=1.3660=0.175

Since the calculated value is greater than the critical value, hence we reject the null hypothesis and conclude that there is a difference among students of different streams in their intention of joining the Club.

__K-S Two Sample Test__

__K-S Two Sample Test__

When instead of one, there are two independent samples then K-S two sample test can be used to test the agreement between two cumulative distributions. The null hypothesis states that there is no difference between the two distributions. The D-statistic is calculated in the same manner as the K-S One Sample Test.

**Formula**

D=Maximum|Fn1(X)−Fn2(X)|D=Maximum|Fn1(X)−Fn2(X)|

Where −

· n1n1 = Observations from first sample.

· n2n2 = Observations from second sample.

It has been seen that when the cumulative distributions show large maximum deviation |D||D| it is indicating towards a difference between the two sample distributions.

The critical value of D for samples where n1=n2n1=n2 and is ≤ 40, the K-S table for two sample case is used. When n1n1 and/or n2n2 > 40 then the K-S table for large samples of two sample test should be used. The null hypothesis is accepted if the calculated value is less than the table value and vice-versa.

Thus use of any of these nonparametric tests helps a researcher to test the significance of his results when the characteristics of the target population are unknown or no assumptions had been made about them.