Explain "statistical power."

Statistical power is defined as the ability of a test to correctly reject the null hypothesis when it is false, which means it effectively detects a true effect that exists in the population. High power indicates that there is a strong probability of finding a statistically significant result when an actual effect or difference is present.

When designing experiments or studies, researchers aim for high statistical power, usually set around 0.8 or 80%. This means that there is an 80% chance of detecting an effect, assuming it truly exists. Factors that influence statistical power include the sample size, the effect size (the magnitude of the difference or relationship being tested), and the significance level set for the hypothesis test.

In contrast, the other options discuss different aspects of hypothesis testing and statistical analysis. The likelihood of Type I error pertains to incorrectly rejecting a true null hypothesis, the proportion of variance explained relates to model fit rather than the power of a hypothesis test, and the amount of data required relates to sample size considerations but does not define the concept of statistical power itself.