Chi- Square is an important non-parametric test and as such now rigid assumptions are necessary is respect of the type of population we require only the degree of freedom for using this test. Non- parametric chi-square test can be used

- As a test of goodness of fit
- As a test of independence

This test was developed by statistician “Karl Pearson”.

As a test of goodness of fit: Chi – square test enable us to explain how does the theoretical distribution fit to the observed data. If the calculated value of the chi-square is less then the table value at a certain level of significance which means that there is no difference between observed and expected frequencies otherwise there is significant difference between expected and observed frequencies.

As a test of independence : Chi-Square test enables us to explain weather or not two attributes are associated. For example we may be interested in knowing weather a new medicine is effective in controlling fever or not. Chi – square test will help in deciding this issue. For this first of all we calculate chi – square value and the value is compared with the table value.

If the calculated value of chi – square is less than the table value at a given level of significance for given degree of freedom we conclude that the two attributes are independent i.e medicine is not effective in controlling fever.

We may apply the chi – square test either as a test of goodness of fit or as a test to judge the significance of an association between attributes. Chi – Square is calculated by the help of following formulae :

Where,

O = Observed frequency

E = Expected frequency

*D.f = (r-1) (c-1)*

Conditions for the application of Chi – square test :

- Observation recorded and used are collected on the random basis.
- All the items in the sample must be independent.
- No – group should contain very few items.
- The overall no if items must be greater than 30. i.e it should normally be at least 50.
- The constraints must be linear