Selection of Appropriate Statistical Methods for Data Analysis: A Review

Choosing the right statistical techniques for analyzing data is essential to obtain accurate and trustworthy results from research efforts. This article gives a detailed summary of the factors that affect the selection of statistical techniques, such as data characteristics, study goals, sample size, data spread, measurement level, assumptions, software access, and researchers' skills. Using case studies and examples, we demonstrate how statistical methods can be applied in real-life situations, showcasing both effective techniques and obstacles faced. Practical advice is given to researchers and practitioners, highlighting the significance of comprehensive exploratory data analysis, validation of assumptions, clear communication of findings, and ongoing learning. Opportunities for future research and development in statistical methodology are pinpointed, such as the advancement of methods for managing intricate data structures and the improvement of software tools and resources. Following proven methods and welcoming new ideas, researchers and professionals can improve the accuracy and dependability of their studies and help advance statistical techniques.