Power of the Test

Sample Size: Increasing the sample size generally increases the power of a test because it provides more data to detect an effect.

Effect Size: Effect size measures the magnitude of the difference or relationship between variables. Larger effect sizes tend to result in higher power.

Type II Error: Type II error occurs when we fail to reject the null hypothesis even though it is false, leading to a false negative result. Power and Type II error are inversely related - as power increases, Type II error decreases.