A fun way to understand false positives and false negatives:
False Positive: This occurs when a model incorrectly predicts something as true when it is not. (Type I error, like predicting a man is pregnant.)
False Negative: This happens when the model fails to detect something that is actually true. (Type II error, like telling a pregnant woman she is not pregnant.)