Type I Error: This refers to incorrectly rejecting a true null hypothesis, meaning we conclude there is an effect or relationship when there isn't.
Type II Error: This occurs when we fail to reject a false null hypothesis, meaning we miss detecting an actual effect or relationship.
Effect Size: Effect size measures the magnitude of the difference or association between variables. A larger effect size increases the power of a statistical test.