By using difference scores for each participant, repeated-measures designs lower the standard error, increase statistical power, and make it easier to detect real treatment effects. In contrast, independent-measures designs retain individual differences between groups, leading to higher variance, larger standard errors, and often lower chances of finding significant differences even when the mean differences are the same.