This paper analyses the concepts of parametric and non-parametric tests used and differentiated between them. The main problem of social science scholars does not know applications of these types of tests. Therefore, my endeavors to clarify the concepts of these tests. This paper is based on theories so secondary data used by secondary sources like internet websites, research papers etc. Parametric tests follow certain assumption where as non Parametric test don’t follow any certain assumptions. There is a wide range of statistical tests. The decision of which statistical test to use depends on the research design, the distribution of the data, and the type of variable. In general, if the data is normally distributed you will choose from parametric tests. If the data are non-normal you choose from the set of non-parametric tests. An application of parametric test the nature of data should follow a normal distribution curve, whereas in non parametric test nature of the data doesn’t follow the normal. This paper clearly defines the concept of parametric and non parametric test and their assumptions of application. In a typical research design there might be statistical errors and shortcomings due to the incorrect use of statistical tools and techniques thereby leading to incorrect result and conclusions. These incorrect results, conclusions may have a negative effect on the reliability, validity and verifiability of the research results.
Cite this article:
Ravindra Bhardwaj. A study of the Theoretical Framework of Parametric and non-Parametric Tests used Social Sciences. Research J. Humanities and Social Sciences. 8(2): April- June, 2017, 225-228. doi: 10.5958/2321-5828.2017.00034.1