False positives can arise due to several reasons, including:
Data Quality Issues: Inaccurate or incomplete data can lead to erroneous conclusions. Statistical Errors: Misuse of statistical tests or misinterpretation of results can lead to false positives. Bias: Confirmation bias or other cognitive biases can lead to interpreting data in a way that confirms pre-existing beliefs. Overfitting: In predictive modeling, overfitting occurs when a model is too closely tailored to a specific dataset, failing to generalize to new data.