Ensuring data quality is crucial for reliable analysis. Key practices include:
Data Cleaning: Removing errors and inconsistencies from datasets. Standardization: Using standardized protocols and formats for data collection. Validation: Cross-checking data with known standards or control samples. Documentation: Keeping detailed records of data sources and processing steps.
