Jun 4, 2025

Structural Error

Structural Errors in Data Analysis

Structural errors are common issues encountered during data analysis and data cleaning processes. These errors can significantly impact the quality and reliability of analytical results.

Types of Structural Errors

Structural errors typically arise from inconsistencies in the way data is recorded or formatted. These errors can include:

• Misspelled categories (e.g., “Male” vs “Mle”)
• Different naming conventions (e.g., “NY” vs “New York”)
• Incorrect data types (e.g., numbers stored as text)
• Unexpected or missing values

What are the list of structural errors?

Structural errors typically arise from inconsistencies in the way data is recorded or formatted. These errors can include:

• Misspelled categories (e.g., “Male” vs “Mle”)
• Different naming conventions (e.g., “NY” vs “New York”)
• Incorrect data types (e.g., numbers stored as text)
• Unexpected or missing values

Causes of Structural Errors

Structural errors often occur due to manual data entry, lack of standardized data collection procedures, or merging datasets from different sources. These errors can be subtle and may not be immediately apparent without careful inspection.

Detecting and Correcting Structural Errors

To ensure data quality, it is essential to identify and correct structural errors. This may involve:

• Standardizing category names
• Converting data types
• Handling missing or unexpected values

What is a structural error?

Structural errors are common issues encountered during data analysis and data cleaning processes. These errors can significantly impact the quality and reliability of analytical results.

What is structural error in data?

Structural errors typically arise from inconsistencies in the way data is recorded or formatted. These errors can include misspelled categories, different naming conventions, incorrect data types, and unexpected or missing values.

Structural Error