One ideal goal of data science is to distill analysis to one number when possible. Then use that number to draw conclusions about your data. But this can be a difficult task when your data set is missing values. Simply ignoring missing data can lead to significant statistical power loss: 35% loss for 10% missing data, 98% loss for 30% missing data. Ignoring missing data can also lead to bias solutions and unreliable parameter estimation. To overcome these challenges statisticians have developed sophisticated techniques to handle the missing data without ignoring them called imputation.
