A hospital research ethics committee is reviewing several clinical trial protocols and post-study analyses. Match each specific trial scenario or parameter description to the statistical concept it best represents.
- A trial concludes that a novel biologic significantly reduces rheumatoid arthritis flares compared to placebo, but later definitive evidence shows the drug is completely ineffective.Type I error ()
- A small pilot study concludes a novel surgical technique does not reduce postoperative infection rates, although the technique truly is superior to the standard approach.Type II error ()
- Before initiating a study, investigators determine there is an 80% probability they will successfully detect a true 2-day reduction in intensive care unit length of stay.Statistical power ()
- Investigators design a trial based on their hypothesis that the new cardiovascular intervention will yield a 15% absolute reduction in 30-day mortality.Expected effect size
Cevap
The scenarios correctly match to: False positive finding = Type I error; False negative finding = Type II error; Probability of detecting a true difference = Statistical power; Anticipated magnitude of benefit = Expected effect size.
A Type I error occurs when researchers incorrectly reject a true null hypothesis (a false positive). A Type II error occurs when researchers fail to reject a false null hypothesis (a false negative). Statistical power () is the probability of correctly rejecting a false null hypothesis. The expected effect size is the clinically meaningful difference researchers anticipate observing between groups.
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Anahtar Kavram
Understanding the definitions and clinical manifestations of Type I errors (false positives), Type II errors (false negatives), statistical power, and effect size in clinical research.