This study focuses on non-parametric analyses for MBA’s. Parametric analyses refer to the analyses of data without the use of parameters. This study presents and evaluates the importance of non-parametric analyses in decision-making process. In particular, it focuses on presenting the weaknesses of parametric methods in assessment of financial performance in organizations. The study reveals that alone, parametric measures are not adequate to indicate the overall level of performance of an organization. It emphasized on the need to use non-parameter non-parametric analyses in enhancing understanding of the overall performance and viability of an organization as an investment destination.
Non-parametric statistics refers to the comparison of data without the application of parameters. There are various things associated with non-parametric statistics. These techniques are usually not based on data that is draw from a given distribution. This presents them as an opposite to the parametric approach to measurement. Under non-parametric methods, data is assumed to be a function from given sample and its analyses is not based on parametric distributions. Usually, statistical hypothesis is based on random variables that assume specified mean as well as variance (Brodskij, & Darchovskij, 1993). Under the non-parametric measures, the model expands with time to accommodate increasing complexity of the measured variable. Often, non-parametric methods results to ordinal outcome. Due to the few assumptions made under non-parametric measures, they are more widely applied in business. The next benefit of non-parametric methods is their simplicity. Nevertheless, non-parametric methods have lesser power. For instance, individuals may be forced to rely on rather large sample size in decision-making. This study focuses on non-parametric measures in measuring of performance of organizations as compared to parametric methods...