When selecting values for statistics, which statement best describes valid values to consider?

When selecting values for statistics, which statement best describes valid values to consider?

• A. Include All Values, Including Those With Missing Fields
• B. Exclude Only Rows With Missing Values
• C. Use Only Complete Records With No Missing Fields
• D. Use Only Valid Values And Exclude Missing Or Invalid Records From Calculations

Answer: (D)

When computing statistics, you want to work with data you can trust. Valid values are real, plausible data points that meet the rules or expectations for the field. Missing values can’t be used directly in most calculations, and records with invalid values (out-of-range, inconsistent, or impossible data) can distort results if included. So the best approach is to use only valid values and exclude missing or invalid records from calculations. If there are many missing values, you might consider imputation or clearly reporting the missingness, but the calculation itself should be based on data you know are valid. Including all values, even with missing fields, can mislead calculations; excluding only rows with missing values leaves invalid data behind; and using only complete records reduces sample size and can bias the results.

When computing statistics, you want to work with data you can trust. Valid values are real, plausible data points that meet the rules or expectations for the field. Missing values can’t be used directly in most calculations, and records with invalid values (out-of-range, inconsistent, or impossible data) can distort results if included. So the best approach is to use only valid values and exclude missing or invalid records from calculations. If there are many missing values, you might consider imputation or clearly reporting the missingness, but the calculation itself should be based on data you know are valid. Including all values, even with missing fields, can mislead calculations; excluding only rows with missing values leaves invalid data behind; and using only complete records reduces sample size and can bias the results.