What happens when a dataset includes records with missing data?
A. It makes downstream processing faster.
B. It normalizes trends by imputing missing data with averages.
C. It adds ambiguity to the analysis process.
D. It nulls the dataset, which must then be discarded and recollected.
E. I don't know this yet.
Correct Answer is: C. It adds ambiguity to the analysis process.
When a dataset includes records with missing data, it can be difficult to accurately analyze the data. In some cases, the missing data can be imputed, or estimated, based on the other data in the dataset. However, imputing data can introduce ambiguity and reduce the accuracy of the data. In other cases, the missing data can be ignored. This can also introduce bias, but it may be the only option if the data is not able to be imputed.