Accept errors is terms during recruitment when a recruiter appoints an individual for a job profile for which who is not qualified, but is appointed for other reasons they feel would benefit the organization. The opposite of accept errors is reject errors.
In statistics, a type I error is the incorrect rejection of a null hypothesis which is true. A type II error is the failure to reject a null hypothesis which is false. Type I errors are known as false positive while type II errors are known as false negative.
A type I error leads a person to a conclusion that a fact exists when it does not.
A type II error leads a person to believe that a fact does not exists when it does exist.
In terms of false positive and false negative, a positive result is when a person rejects a null hypothesis while a negative result occurs when a person fails to reject a null hypothesis. So here positive= alternative and negative = null or the vice versa depending on the situation. So in these terms, a type I error is false positive (incorrectly choosing alternative hypothesis instead of null hypothesis) and a type II error is a false negative (incorrectly choosing the null hypothesis instead of the alternative hypothesis).