Detecting data manipulation requires a rigorous approach, including:
Peer Review: Independent scrutiny by other experts can help identify inconsistencies or anomalies in the data. Reproducibility Tests: Ensuring other researchers can replicate the results using the same methodology. Statistical Analysis: Employing advanced statistical techniques to detect patterns indicative of manipulation. Transparency: Encouraging open access to raw data and methodologies for verification by the scientific community.
