Statistical power is the probability that a test will correctly reject a false null hypothesis, essentially measuring the test's sensitivity to detect an effect when there is one. It is influenced by factors such as sample size, effect size, significance level, and variability within the data.
Individual differences refer to the distinct variations among people in terms of behavior, cognition, emotion, and personality, which are shaped by a combination of genetic, environmental, and social factors. Understanding these differences is crucial in fields like psychology and education to tailor approaches that accommodate diverse needs and enhance personal development.
A within-subjects factor is a variable that is manipulated or measured within the same group of participants across different conditions or time points, allowing each participant to serve as their own control. This design increases statistical power by reducing variability due to individual differences, but it can introduce order effects that need to be controlled through counterbalancing or other methods.