How to best present data that is incomplete?

1. How do you deal with incomplete data?
2. What is the best way to handle missing data?
3. What should a researcher do with incomplete answers or missing data?
4. Can incomplete data be accurate?

How do you deal with incomplete data?

When dealing with missing data, data scientists can use two primary methods to solve the error: imputation or the removal of data. The imputation method develops reasonable guesses for missing data. It's most useful when the percentage of missing data is low.

What is the best way to handle missing data?

One way of handling missing values is the deletion of the rows or columns having null values. If any columns have more than half of the values as null then you can drop the entire column. In the same way, rows can also be dropped if having one or more columns values as null.

What should a researcher do with incomplete answers or missing data?

Researchers might simply discard any record (e.g. questionnaire or claim file) that is missing information. Or they might “fill in” the missing data using what are called “imputation,” weighting or model-based procedures.

Can incomplete data be accurate?

If the data is missing, the information cannot be validated and if it's not validated, it cannot be considered accurate.