A data analyst is exploring a CustomerOrders dataset that includes OrderID, ShipDate, and ReturnDate columns. The analyst runs a check for missing values and discovers that the ReturnDate column has a high percentage of nulls. Upon investigating, the analyst finds that a ReturnDate is null if, and only if, an order has not been returned. Which of the following is the BEST description of this situation?
A failure in the data collection system is preventing ReturnDate from being recorded for a large subset of orders.
The ReturnDate column should be imputed with the ShipDate to ensure the dataset is complete for modeling.
The missing values are structurally expected and indicate that the ReturnDate is not applicable to those orders.
The data is Missing Completely at Random (MCAR), and the nulls should be removed before analysis.
The correct answer describes the situation as structurally expected missing values. This type of missing data occurs when a value is absent for a logical reason. In this scenario, an order that has not been returned cannot have a ReturnDate, so the value is intentionally and correctly left null. Recognizing this is a key part of data exploration.
A system failure is unlikely because the pattern of missingness is systematic and logical, not random or erroneous. The data is not Missing Completely at Random (MCAR); in fact, the missingness is perfectly predictable based on another attribute (the return status of the order). Finally, imputing the ReturnDate with the ShipDate is an inappropriate data transformation technique that would introduce incorrect information into the dataset, as it wrongly implies that an item was returned the same day it was shipped.
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What are structurally expected missing values in a dataset?
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How can a data analyst differentiate between structurally expected missing values and data collection errors?
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Why is imputing the `ReturnDate` with another column like `ShipDate` inappropriate in this scenario?
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CompTIA Data+ DA0-002 (V2)
Data Acquisition and Preparation
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