CompTIA Data+ (DA0-002)Data AnalysisEasy
A data analyst has identified that a significant portion of their sales data is missing due to a technical glitch for a specific month. Simply deleting the records with missing values would lead to a substantial loss of valuable information and potentially bias the analysis. To address this, the analyst wants to estimate and fill in these missing values using a method that maintains the dataset's integrity as much as possible. Which technique is most appropriate?
- AOutlier detection
- BData aggregation
- CFeature selection
- DData imputation
Show answer & explanationAnswer & explanation
Correct answer: D. Data imputation
Data imputation is the process of estimating and filling in missing values in a dataset. Various methods exist, such as mean, median, or mode imputation, or more advanced techniques like regression imputation, which help maintain the dataset's integrity when deleting records is not feasible.
Why the other options are wrong
- A. Outlier detection identifies unusual data points, not missing ones.
- B. Data aggregation involves summarizing data, not filling in missing entries.
- C. Feature selection chooses a subset of relevant features, not for handling missing data.
Data Imputation
The process of replacing missing data with substituted values (estimates) to enable complete data analysis without discarding records.
- Common methods include mean, median, mode, or regression imputation.
- Helps preserve sample size and statistical power.
- Choice of method depends on the nature of missingness and data type.
Memory trick: Imputation is like a data detective filling in the blanks to complete the story.