The statistical error (said to be "of the second kind" or beta error) made in testing an hypothesis when it is concluded that a treatment or intervention is not effective when it really is. Sometimes referred to as a false negative.

An incorrect decision to accept something when it is unacceptable.

The error of not rejecting a false null hypothesis. With respect to manager selection, firing (or not hiring) managers with positive value-added.

The failure to reject a null hypothesis when it is false; it is also known as beta error.

The error that is committed when a false null hypothesis is accepted erroneously. The probability of a Type II Error is abbreviated with the uppercase Greek letter beta.

An error created by accepting the null hypothesis when it is false (i.e., investigators conclude that no relationship exists between variables when, in fact, a relationship does exist).

the error of failing to refute the null hypothesis whenever it is actually not authentic. Investigators make this error if they conclude that a specific impact or union doesn't exist whenever it [..]

A Type II Error is also known as a False Negative or Beta Error. This happens when you accept the Null Hypothesis when you should in fact reject it. The Null Hypothesis is simply a statement that is t [..]

An error that occurs when a researcher concludes that no significant relationship between two variables (based on analysis of sample data) when in fact the relationship does exist in the population fr [..]

(beta error) – Occurs when it is concluded that an observed difference between groups was due to chance when it fact it was due to the effects of the independent variable (i.e., a false null hypothesis is accepted).

Is made when the null hypothesis, H0, is not rejected when in fact a specific alternative hypothesis, H1, is assumed true. The probability of making a type II error is denoted by beta (b). For example [..]

Is made when the null hypothesis, H0, is not rejected when in fact a specific alternative hypothesis, H1, is assumed true. The probability of making a type II error is denoted by beta (b). For example [..]

an error when one concludes that there is not a real difference between two means when, in fact, there is a difference; also called a false negative error

Type II Error occurs in statistical hypothesis testing when the null hypothesis is incorrectly accepted. Type II errors are also known as 'false negatives'; they are the failure to detect a [..]

The error of accepting the null hypothesis (H0) when it is actually false. The power of the test is the probability of not committing a Type II error (see Type I Error).

An error of statistical inference when the null hypothesis is retained when it is false. This is an error of "not seeing enough in the data."

is the error of accepting the credibility of a M/S when in fact the M/S is not sufficiently credible.

In statistics the accepting of a false hypothesis.

The failure to reject the null hypothesis when it is false.

Acceptance of a null hypotheses when, in fact, it is false. The Type II error rate is the probability that the null hypothesis (that there is not a difference between the experimental and control grou [..]

That is, 'type two error.' This is the error in testing a hypothesis of failing to reject a hypothesis when it is false.