Dealing with missing data
You might have already encountered missing data in your studies or career while performing any kind of data analysis. Missing data is a very common issue that you will encounter in most datasets. It's extremely rare to find a "perfect" dataset. Missing data is not just a nuisance. It is a serious problem that you need to account for as it can affect your results.
What is missing data?
Before you can learn how to deal with missing data, first, you need to understand each of its three types:
- Missing at Random (MAR): This refers to data that is missing due to other variables you have information about. For example, in a survey, if you found that some specific demographics show a tendency to not reply to a question, then the missing data is considered to be MAR. An easy way to remember this is that if you can explain why the data is missing by using other variables, but not to the value of the variable with missing values itself, then...
