The probability of making a type I error is denoted by α, and the probability of making a type II error is denoted by β.
Although most applications of hypothesis testing control the probability of making a type I error, they do not always control the probability of making a type II error.
Quantum mechanics prescribes a means of calculating the probability of making these transitions.
No longer could physicists speak with confidence of physical reality, but only of the probability of making certain measurements.
A research unit and a customer can invest to increase the probability of making an innovation.
That is, the probability of making transition to a less healthy state does not increase with the age.
A graph known as an operating-characteristic curve can be constructed to show how changes in the sample size affect the probability of making a type II error.
We will present a statistical analysis of the probability of making such measurements using nominal orbital parameters of the relevant spacecraft.
SMTs are designed with the goal of maximizing the probability of making correct classification decisions (i.e., mastery and nonmastery) while at the same time minimizing test length.
A priori power analysis enables researchers to estimate the probability of making a Type II error (i.e., finding no significant difference when one in fact exists).
As a result, the probability of making another hop is low.
