Final answer:
The advantage of a powerful experiment is that it increases the likelihood of detecting actual effects of the independent variable. This is achieved by maximizing the power of the test, which is the ability to correctly identify true effects when they exist.
Step-by-step explanation:
The advantage of a powerful experiment is that you are more likely to detect the real effects of the independent variable, if there are any. The power of the test is defined as 1 – ß, where ß represents the probability of a Type II error—failing to reject the null hypothesis when the alternative hypothesis is true. A high power means that the test is more sensitive to detecting actual effects when they exist, which is why increasing power is often achieved by increasing the sample size (N), keeping the significance level (α) constant. This also indirectly helps to reduce the likelihood of a Type II error.
It's necessary to distinguish between the types of errors: α denotes the probability of a Type I error, which occurs when the null hypothesis is incorrectly rejected; whereas ß represents the probability of a Type II error, which happens when the null hypothesis is not rejected even though the alternative hypothesis is true.