Statistical power is the probability that a statistical test will correctly reject a false null hypothesis. In simpler terms, it measures a test's ability to detect an effect when there is one. A higher statistical power means a greater chance of finding a true effect, which is crucial in research to avoid Type II errors, where researchers fail to identify a significant result. Several factors influence statistical power, including sample size, effect size, and significance level. Increasing the sample size generally enhances power, as larger samples provide more accurate estimates of the population. Additionally, a larger effect size makes it easier to detect differences, further boosting power in studies involving hypothesis testing.