How to handle missing values in experimental data?

Missing values in experimental data require deliberate methodological choices to preserve analytical validity and avoid compromising inferences. Addressing missingness is feasible through various strategies depending on data characteristics and research goals. Key principles involve understanding the mechanism of missingness (MCAR, MAR, MNAR) to select appropriate techniques, assessing potential bias introduced by the chosen method, and ensuring transparency in reporting. Common techniques include deletion methods (listwise, pairwise) and imputation methods (mean/median substitution, k-nearest neighbors, multiple imputation). Selection depends on the proportion of missing data, their pattern, and the analysis goals. Crucially, the chosen method should align with statistical assumptions to prevent distorted results. Implementing a solution involves sequential steps: firstly, diagnose the pattern and extent of missingness; secondly, choose the most suitable technique based on the diagnosed pattern and research context; thirdly, apply the method carefully, often employing validated software packages; finally, conduct sensitivity analyses to evaluate the impact of missing data handling on the conclusions. Careful handling preserves sample size, maximizes statistical power, and enhances the robustness and credibility of experimental findings across scientific and industrial applications.