In catalysis research, the accuracy of experimental data is crucial for understanding reaction mechanisms, optimizing catalytic processes, and designing new catalysts. Missing data can lead to incorrect conclusions or suboptimal designs. By imputing missing values, researchers can maintain the integrity of the dataset and ensure robust statistical analysis, which is essential for making informed decisions.
