Cross verification is crucial for several reasons:
Reliability: By comparing findings across multiple datasets, researchers can confirm that the results are not anomalies. Validity: It helps in validating the causal relationships identified in the study. Bias Detection: It aids in identifying and mitigating potential biases that may arise due to sampling errors, measurement errors, or other confounders.
