Chi – Square test is a test where the samples of tests statistics are distributed on the basis of chi – squared distribution. Chi – square test is a hypothesis test. If the null hypothesis comes to be true then we can make the sample data distribution as close to chi – squared distribution as desired by us. This can be done just by increasing the size of sample as per the chi data requirement. In some cases chi – square test is valid by approximation only. For example McNemar’s test, Cochran – Man – Haenszel chi – square test, the time series analysis by the portmanteau test in the time series, and linear by linear association chi – squared test etc. Under only one circumstance the distribution of statistics test comes out to be exact chi – square test, that is, the test where the variance of normally distributed population has an absolute value which is based on a sample data. This is just hypothetical example which cannot be found in actual practical. From a population having normal distribution we take a sample size x, upon making a test which describes whether the variance has a predetermined value or not. We have a well known result which reflects the significance level and the interval.