Missing data can significantly skew results, leading to biased conclusions and reduced statistical power. In cancer studies, where precise measurements are critical for understanding the disease's progression, treatment efficacy, and patient outcomes, accurate imputation ensures that analyses remain robust and reliable. This is particularly important in large-scale studies like genomic data analysis and clinical trials.
