Final answer:
D. Power
The probability of finding a true difference between two samples is referred to as the power of a test. This represents the test's ability to detect an actual effect or difference when it truly exists, with a high power indicating a higher likelihood of correctly identifying true differences.
Step-by-step explanation:
The probability of finding a true difference between two samples is known as the power of a test. The power is the likelihood that a test will detect an actual difference when one truly exists. This involves rejecting the null hypothesis when it is false, therefore avoiding a Type II error. Notably, a high power is desirable as it means there's a greater chance of identifying true differences between samples.
For instance, to illustrate power, imagine that we're conducting a test to determine if a new medication is more effective than the existing one. The power of our test would be our ability to detect the efficacy difference if the new drug really is better. If our test has 90% power, it means that if there is a genuine difference to be observed, our test will find this difference 90% of the time. In statistical terms, Type I error is the risk of mistakenly rejecting a true null hypothesis, while Type II error pertains to the risk of not rejecting a false null hypothesis.
Importantly, confidence intervals also play a role in hypothesis testing. A 90 percent confidence interval refers to the range within which we expect the true population parameter to fall 90% of the time, assuming we could take repeated samples.