Detecting data contamination involves several strategies:
1. Data Cleaning: Regular checks and cleaning procedures can help identify and rectify errors. 2. Statistical Methods: Techniques such as outlier detection and sensitivity analysis can be used to identify anomalous data points. 3. Validation: Cross-checking data with other reliable sources can help detect inconsistencies. 4. Audit Trails: Keeping detailed records of data collection and entry processes can help identify where contamination may have occurred.
