Why and When Statistics is Required, and How to Simplify Choosing Appropriate Statistical Techniques During Ph.D. Program in India?

Purpose: The purpose of this article is to explain the key reasons for the existence of statistics in doctoral-level research, why and when statistical techniques are to be used, how to statistically describe the units of analysis/samples, how to statistically describe the data collected from units of analysis/samples; how to statistically discover the relationship between variables of the research question; a step-by-step process of statistical significance/hypothesis test, tricks for selecting an appropriate statistical significance test, and most importantly which is the most user-friendly and free software for carrying out statistical analyses. In turn, guiding Ph.D. scholars to choose appropriate statistical techniques across various stages of the doctoral-level research process to ensure a high-quality research output. Design/Methodology/Approach: Postmodernism philosophical paradigm; Inductive research approach; Observation data collection method; Longitudinal data collection time frame; Qualitative data analysis. Findings/Result: As long as the Ph.D. scholars can understand i) they need NOT be an expert in Mathematics/Statistics and it is easy to learn statistics during Ph.D.; ii) the difference between measures of central tendency and dispersion; iii) the difference between association, correlation, and causation; iv) difference between null and research/alternate hypotheses; v) difference between Type I and Type II errors; vi) key drivers for choosing a statistical significance test; vi) which is the best software for carrying out statistical analyses. Scholars will be able to (on their own) choose appropriate statistical techniques across various steps of the doctoral-level research process and comfortably claim their research findings. Originality/Value: There is a vast literature about statistics, probability theory, measures of central tendency and dispersion, formulas for finding the relationship between variables, and statistical significance tests. However, only a few have explained them ...