False positive, False negative, Type I error, Type II error

	True	False
True	Correct	Type II error
False	Type I error	Correct

Binary classification

	Actual Class Positive	Actual Class Negative
Assigned Positive	True Positive	False Positive
Assigned Negative	False Negative	True Negative

Statistics

A positive result corresponds to rejecting the null hypothesis, while a negative result corresponds to failing to reject the null hypothesis; “false” means the conclusion drawn is incorrect. Thus a type I error is a false positive, and a type II error is a false negative. Wikipedia

	null hypothesis($H_0)$ True	null hypothesis($H_0)$ False
Fail to reject	Correct (True Negative) (1-$\alpha$, confidence level)	Type II error (False Negative) ($\beta$)
Reject	Type I error (False Positive) ($\alpha$ , significance level)	Correct (True Positive) ($1-\beta$, power)