In Looker, what type of measure is typically used to compute median values?

In Looker, what type of measure is typically used to compute median values?

• A. Aggregate measures
• B. Non-aggregate measures
• C. All measures
• D. List measures

Answer: (A)

In Looker, the correct choice for computing median values is typically an aggregate measure. Aggregate measures are designed to perform calculations that summarize or combine data from multiple rows into a singular value. When it comes to median values, they are computed by evaluating a set of data points and determining the middle point—an operation that involves gathering a group of values to derive an aggregate result. To effectively calculate the median, Looker uses aggregate functions, as each function must consider the entire dataset to find the central value accurately. This involves sorting the values and determining the middle value or, in cases where the dataset has an even number of entries, averaging the two middle values. Non-aggregate measures, on the other hand, operate on a row-by-row basis and do not perform any summarization, making them unsuitable for calculating a median. The term "all measures" is too broad and does not specifically indicate the type of measure suited for this calculation. List measures specifically return a list of unique values rather than performing calculations on aggregates. Therefore, aggregate measures are the appropriate choice when computing median values in Looker.

In Looker, the correct choice for computing median values is typically an aggregate measure. Aggregate measures are designed to perform calculations that summarize or combine data from multiple rows into a singular value. When it comes to median values, they are computed by evaluating a set of data points and determining the middle point—an operation that involves gathering a group of values to derive an aggregate result.

To effectively calculate the median, Looker uses aggregate functions, as each function must consider the entire dataset to find the central value accurately. This involves sorting the values and determining the middle value or, in cases where the dataset has an even number of entries, averaging the two middle values.

Non-aggregate measures, on the other hand, operate on a row-by-row basis and do not perform any summarization, making them unsuitable for calculating a median. The term "all measures" is too broad and does not specifically indicate the type of measure suited for this calculation. List measures specifically return a list of unique values rather than performing calculations on aggregates. Therefore, aggregate measures are the appropriate choice when computing median values in Looker.