Non-parametric Methods in Analysis of Experimental Data
نویسنده
چکیده
1. Introduction In conventional setup the analysis of experimental data is based on the assumptions like normality, independence and constant variance of the observations. However, there may arise experimental situations where these assumptions, particularly the assumption of normality, may not be satisfied. In such situations non-parametric test procedures may become quite useful. A lot of attention is being made to develop non-parametric tests for analysis of experimental data. Most of these non-parametric test procedures are based on rank statistic. The rank statistic has been used in development of these tests as the statistic based on ranks is 1. distribution free 2. easy to calculate and 3. simple to explain and understand. The another reason of use of the rank statistic is due to the well known result that the average rank approaches normality quickly as n (number of observations) increases, under the rather general conditions, while the same might not be true for the original data {see e.g. Conover and Iman (1976)}. The non-parametric test procedures available in literature cover completely randomized designs, randomized complete block designs, balanced incomplete block designs, design for bioassays, split plot designs, cross-over designs and so on. For excellent and elaborate discussions on non-parametric tests in the analysis of experimental data, one may refer In the present talk we shall concentrate on the non-parametric test procedure for analysis of one-way and two-way classified data. Some of these procedures are: 1. In the sequel we describe each of these tests in brief. 2. Non-parametric Procedures for the Analysis of Experimental Data In this section, we shall describe the non-parametric tests for analysis of experimental data generated through completely randomized designs (CRD) and block designs. We begin with the test procedure for the experimental data generated through CRD
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