False positives and false negatives are errors in binary classification systems where a false positive indicates an incorrect identification of a condition or attribute, and a false negative means a failure to identify it when it is present. These errors are critical in evaluating the performance of models, as they impact the precision, recall, and overall accuracy of predictive analytics.