Abstract:
In this study, we are going to evaluate the effect of employing diverse missing value imputation strategies on real datasets while conducting statistical analysis. It will concentrate on Missing Completely at Random (MCAR) data, which provides an opportunity for a thorough assessment of imputation methods. Specific methods examined were mean and median imputation plus other conventional statistical ways of treating missing data. As a result, the research underlines that adequate data management strategies are key to preserving both the credibility and accuracy of scientific analyses. This study demonstrates how Excel can be used as the primary analytical tool to give applied researchers from different areas of specialization practical guidance on method choice when faced with missing data. In the end, these results demonstrate how carefulencial efforts are essential in this field.
