In data cleaning, what is commonly done if no appropriate values can be found for missing data?

In data cleaning, what is commonly done if no appropriate values can be found for missing data?

• A. Fill in with mean values
• B. Remove the whole record
• C. Normalizing the dataset
• D. Consult external sources

Answer: (B)

In the practice of data cleaning, when appropriate values for missing data are not available, removing the entire record is a common approach. This is particularly relevant if the missing data is significant and could impact the overall analysis or if the record contains too much missing information to be valid. By eliminating incomplete records, analysts can maintain the integrity and quality of the dataset, ensuring that subsequent analyses are based on complete data points. While other methods, such as filling in with mean values, normalizing the dataset, or consulting external sources, may also be employed for handling missing data, these techniques may not be suitable in every situation. Filling in with mean values can introduce bias, normalizing does not address missing values directly, and consulting external sources may not be possible or practical in every case. Therefore, removing the whole record can be the most straightforward and effective method when no suitable replacements for the missing data can be found, thus preserving the quality of the dataset for analysis.

In the practice of data cleaning, when appropriate values for missing data are not available, removing the entire record is a common approach. This is particularly relevant if the missing data is significant and could impact the overall analysis or if the record contains too much missing information to be valid. By eliminating incomplete records, analysts can maintain the integrity and quality of the dataset, ensuring that subsequent analyses are based on complete data points.

While other methods, such as filling in with mean values, normalizing the dataset, or consulting external sources, may also be employed for handling missing data, these techniques may not be suitable in every situation. Filling in with mean values can introduce bias, normalizing does not address missing values directly, and consulting external sources may not be possible or practical in every case. Therefore, removing the whole record can be the most straightforward and effective method when no suitable replacements for the missing data can be found, thus preserving the quality of the dataset for analysis.