The Implications of Parametric and Non-Parametric Statistics in Data Analysis in Marketing Research
Dr. Egboro Felix O.

Abstract
Statistical needs of science, technology and governments had grown. Reviewers of research reports, frequently criticize the choice of statistical methods. Researchers ought to have a large suite of statistical techniques to be successful in analyzing data. Analysis can be viewed as the categorization, the aggregation into constituent parts, and manipulation of data to obtain answers to the research questions or questions underlying the research project. A special aspect of analysis is interpretation, which involve taking the results of analysis and making inferences relevant to the research relationship studied, and drawing managerially useful conclusions about these relationships. In a typical research work, there might be statistical errors and short comings due to the incorrect use of statistical test thereby leading to incorrect conclusions. These incorrect conclusions may have negative effect on the reliability, validity and verifiability of the research results. The focus of this paper is on the nature of data and the correct statistical techniques in relation to parametric and non-parametric statistics that amount to reliable and valid research results. Recommendations were made as per the best inferential statistics that lead to reliable business/marketing decisions, putting into consideration the implications of robustness

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