What is the most common method to address missing data values?

Inputting the average or expected value is a widely used method for handling missing data points, primarily due to its simplicity and effectiveness in preserving the dataset's size. By replacing missing values with the average, you can maintain the overall structure of the dataset and ensure that any analyses performed are not based on incomplete information. This technique allows analysts to work with a fuller dataset, which can be particularly beneficial in maintaining statistical power for certain types of analysis.

Using the average is particularly effective in scenarios where the omitted data points are assumed to be missing at random, meaning that the missingness does not depend on the data itself. This approach helps prevent bias that might occur from simply omitting records or using more complex methods that may not always be necessary.

Other methods, such as omitting records with missing values or flagging missing values, may lead to loss of valuable data, especially if the missing values represent a significant portion of the dataset. Using interpolation techniques can be beneficial in specific contexts, such as time series analysis; however, it requires additional assumptions about the nature of the data. In contrast, inputting the average is straightforward, easy to implement, and does not require advanced statistical techniques, making it a common choice among practitioners handling missing data.