Understanding missing data

Data can be missing for a variety of reasons, such as unexpected power outages, a device that got accidentally unplugged, a sensor that just became defective, a survey respondent declined to answer a question, or the data was intentionally removed for privacy and compliance reasons. In other words, missing data is inevitable.

Generally, missing data is very common, yet sometimes it is not given the proper level of attention in terms of formulating a strategy on how to handle the situation. One approach for handling rows with missing data is to drop those observations (delete the rows). However, this may not be a good strategy if you have limited data in the first place, for example, if collecting the data is a complex and expensive process. Additionally, the drawback of deleting records, if done prematurely, is that you will not know if the missing data was due to censoring (an observation is only partially collected) or due to bias (for example, high-income...