The following figure illustrates the principle of power analysis, which consists of four key components: Power, Alpha, sample size, and effect size (d).
First, select any of these components, such as Power, and adjust the other three to observe how changes affect it. This process helps you understand the relationships among these elements and the overall concept of power analysis.
Second, Power represents the probability of correctly rejecting a null hypothesis (H0), expressed as 1 − β, where β is the probability of a Type II error (falsely accepting H0). Alpha (α), or significance level, indicates the probability of incorrectly rejecting a true null hypothesis, typically set at 0.05.
Third, sample size refers to the number of observations in the study, while effect size (d) indicates the magnitude of difference from the null hypothesis.
Finally, when Alpha and effect size are held constant, increasing the sample size results in a higher Power—larger samples provide more convincing evidence that "what is wrong is indeed wrong." Conversely, if sample size and effect size remain constant, a higher Alpha leads to an increase in Power.