Statistical power in a hypothesis test is the probability that the test can detect an effect that truly exists. If an effect truly exists at the population level, it’s entirely possible that a test based on a sample can fail to detect this effect. The higher the power, the more likely the test can detect a true effect.
A variety of factors affect the power of a test including the sample size, the effect size, and the inherent variability in the data. Of these factors, you have the most control over the sample size.
It is important to estimate the power of a test before you begin a study to help ensure that you have a reasonable chance of detecting an effect if one exists.