Imagine a clinical psychologist was testing the effectiveness of a new therapeutic technique for depression (where the null hypothesis would be that the technique had no effect on the depression). They obtain initial results, but later decide the conclusions based on the initial results represent a Type I error. What would that mean?

Question

BlackTom AI · Accepted Answer

When evaluating hypotheses in a clinical psychology study, a Type I error occurs when you reject the null hypothesis (that there is no effect) even though it is actually true.

Option analysis:
- The statement: 'Initial results suggested that the treatment was effective when it really was not.' This precisely captures a false positive scenario. It means the data led researchers to conclude there is an effect of the therapeutic technique, but in reality there is no true effect. This aligns with the definition of a Type I error, which is about incorrectly finding an effect when none exists.

Notes on potential alternatives (though not provided here):
- If the null hypothesis is true and you fail to reject it, that would be a Type II error (a false negative), not a Type I error.
- Concerns about sample size, p-values, or confidence intervals can influence the probability of committing a Type I error, but the core concept remains the same: declaring an effect where there is none.
- The idea of adjusting for multiple comparisons or stopping rules can affect Type I error rates, but the fundamental incorrect conclusion is the same false-positive scenario described in the option.

In summary, the option describes the essence of a Type I error: concluding that the treatment works when, in reality, it does not.

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