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Testing Statistical Assumptions in Research
book

Testing Statistical Assumptions in Research

by J. P. Verma, Abdel-Salam G. Abdel-Salam
April 2019
Intermediate to advanced content levelIntermediate to advanced
224 pages
5h 28m
English
Wiley
Content preview from Testing Statistical Assumptions in Research

1Importance of Assumptions in Using Statistical Techniques

1.1 Introduction

All researches are conducted under certain assumptions. Validity and accuracy of findings depends upon whether we have fulfilled all the assumptions of data and statistical techniques used in the analysis. For instance, in drawing a sample, simple random sampling requires the population to be homogeneous while stratified sampling assumes it to be heterogeneous. In any research, certain research questions are framed that we try to answer by conducting the study. In solving these questions, we frame hypotheses that are tested with the help of the data generated in the study. These hypotheses are tested using some statistical tests, but these tests depend upon whether the data is nonmetric or metric. Different statistical tests are used for nonmetric and metric data for answering same research questions. More specifically, we use nonparametric tests for nonmetric data and parametric tests for metric data. Thus, it is essential for the researchers to understand the type of data generated in their studies. Parametric tests no doubt provide more accurate findings than the nonparametric tests, but they are based upon one common assumption of normality besides some specific assumptions associated with each test. If normality assumption is severely violated, the parametric tests may distort the findings. Thus, in research studies, assumptions are focused on two spheres: data and statistical tests besides methodological ...

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ISBN: 9781119528418Purchase book