Final answer:
Power refers to the likelihood that a statistical test will accurately support the alternative hypothesis when it is indeed true, given as 1 minus the probability of a Type II error. A higher power indicates a more reliable test that's better at detecting true effects.
Step-by-step explanation:
Power is the probability that d) If the research hypothesis is true, the experiment will support it. Power is expressed as 1 – ß, where ß represents the probability of making a Type II error, and power therefore quantifies the likelihood that the test will correctly reject the null hypothesis when the alternative hypothesis is true. A higher power means that there's a lower chance of making a Type II error, and as such, is an indication that the test is more reliable. In hypothesis testing, we aim to have a high test power to ensure that we accurately detect true effects when they exist.
It's important to note that a Type I error, represented by alpha (α), occurs when the null hypothesis is incorrectly rejected when it is actually true. The goal is to minimize both Type I and Type II errors to ensure the reliability of the conclusions drawn from the test.