NON PARAMETRIC TESTS
All practical data follow normal distribution under such situations can estimate the parameters such as mean variance etc ,,, and use the standard test they are known as parametric test. The practical data may be non normal and or it may not be possible to estimate the parameter of the data. The tests which are sued for such situations are called nonparametric tests. Since these tests are based on the data which are free distribution and parameter these tests are known as nonparametric tests.
The nonparametric tests require less calculation because there is no need to compute parameters. Also these test can be applied to very small samples more specifically during pilot studies in market research.
The test techniques makes use of or more values obtained from sample data often called test statistic to arrive at a probability statement about the hypothesis. But no such assumptions are made in case of nonparametric tests. In a statistical test two kinds of assertions are involved viz... an assertion directly related to the purpose of investigation and other assertions to make a probability statement. The former is an assertion to be tested and is technically called a hypothesis whereas the set of al other assertions is called the model. When we apply a test to ( to test the hypothesis ) without a model it is known as distribution free test or the non parametric test. Non parametric tests do not make an assumption about the parameters of the population and thus do not make an assumption distribution. In other words under nonparametric or distribution free tests we do not assume that a particular distribution is applicable or that a certain value is attached to a parameter of the population.