A STUDY ON THE MULTI –SAMPLE ADAPTIVE TESTS AND ITS APPLICATIONS USING AN EXAMPLE

Many adaptive tests have been developed in an effort to improve the performance of tests of significance. We will consider a test of significance to be "adaptive" if the test procedure is modified after the data have been collected and examined. They are carefully constructed so that they maintain their level of significance. That is, a properly constructed adaptive test that is designed to maintain a significance level of will have a probability of rejection of the null hypothesis at or near when the null hypothesis is true. Hence, adaptive tests are recommended because their statistical properties are often superior to those of traditional tests. Theoretical statisticians have all too often accepted a model and considered many statistical inferences using that model without checking its validity. That is, we have accepted a dogma of normal distribution and routinely performed the appropriate statistical inference. Possibly a better way to proceed would be to assume that an appropriate model is to found among a number of models, say, which are suitably placed throughout the spectrum of possible models. Then use the data to select the model which seems most appropriate and with his model and the same data, make the desired inference