How to deal with invalid data in experiments?

Invalid experimental data refers to unusable or erroneous results, which must be systematically identified and addressed to preserve research integrity. Its management is both feasible and essential for robust analysis. Detection requires pre-defined validity criteria based on the experimental design and measurement techniques. Common sources include instrumentation errors, sample contamination, protocol deviations, or outliers exceeding statistical thresholds. Verification involves cross-checking data sources, repeating measurements if possible, and assessing instrument calibration logs. Crucially, document all identified invalid data and the rationale for exclusion to ensure transparency. Flag suspected invalid entries during initial data review. Apply consistent, predefined rules: either exclude data points if irredeemably flawed and justifiable, or apply imputation techniques (e.g., mean substitution, regression-based imputation) cautiously only when appropriate and clearly noted. Always re-assess the impact on statistical conclusions post-treatment. Transparently reporting the type, volume, and handling method of invalid data in the final research communication is mandatory for reproducibility and scientific credibility.