A drug maker claims its drug is effective because a trial showed improved outcomes.

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The key idea is that to claim a drug is truly effective based on a trial, the results must generalize to the people who would actually use the drug, and they should be precise. A large and representative sample provides that. A big sample reduces sampling error, giving a more accurate estimate of the true effect size, and representativeness means the participants resemble real-world patients in age, health status, and other factors. Together, these factors make the observed improvement more likely to reflect real-world benefits across the population. While a double-blind design helps prevent biases in how outcomes are measured, it doesn’t by itself guarantee that the drug will work for everyone who would use it. Observing that the effect lasts for six months shows durability, which is valuable, but it doesn’t by itself prove that the initial improvement would occur broadly or that the trial findings would generalize.