Understanding Statistical Tests Through Distribution Assumptions and Practical Research Applications
Results from statistical tests are used to decide if any differences, relationships, or patterns observed in a set of data are meaningful or, if random, an inevitable consequence of sampling variation. There are two commonly used types of Quantitative Techniques used in Quantitative Analysis which are parametric and non-parametric. The different categories use different assumptions and are applicable to different research contexts. Knowledge of the appropriate choice of a parametric and non-parametric test should be given importance in terms of data type, characteristics of the data distribution, the structure of the sample, the scale of measurement and the goals of the research. Good testing results enhance the accuracy of analysis and the robustness of statistical interpretations. Distribution Assumptions Assumptions about distribution are important as they guide the selection of a statistical procedure. Normality is often checked, especially for smaller sample sizes, whe...