A repeated-measures test usually is more likely to detect a real treatment effect than an independent-measures test because the repeated-measures design typically has a smaller variance and a smaller estimated standard error.

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The repeated-measures design reduces the variability by controlling for individual differences, making it more likely to detect a treatment effect. As a result, the estimated standard error is smaller, increasing the power of the test.

A Repeated-Measures Test Usually Is More Likely to Detect a Real