Question

Explain what should be done with suspected or missing data?

A) Include it in the analysis without any adjustments
B) Exclude it from the analysis to avoid bias
C) Impute missing values using statistical methods
D) Report the missing data but take no further action

Answer 1

The best practice for handling suspected or missing data is to impute values using statistical methods and to report the occurrence of any missing data or outliers. Including without adjustment or excluding it entirely could lead to bias.

Step-by-step explanation:

When handling suspected or missing data, several strategies can be applied, but including it in the analysis without adjustments is not recommended as it could skew the results. Excluding the data entirely can also lead to bias, particularly if the absent data has a pattern, such as missing information from a specific segment of the population. Imputing missing values using statistical methods is preferred, as this process can help to maintain the integrity of the data set by estimating what the missing data should be based on other available data. Careful examination of outliers is necessary to decide whether they should be kept or removed. If an outlier is due to an error, it may need to be excluded, whereas real outliers that reflect the population should not be discarded without consideration. Additionally, if data is found to be missing or deemed an outlier, reporting the occurrence of the missing data or outliers is crucial to preserving the transparency of the analysis.