Final answer:
An independent-measures design involves comparing two independent samples, which is different from a study that compares a single sample mean to a population mean. This is particularly common in experiments comparing two different groups on a specific outcome.
Step-by-step explanation:
An independent-measures design, also known as an independent groups design, is different from a study that makes inferences about the population means from a sample mean because in an independent-measures design, there are two independent samples that are compared to one another. This type of statistical analysis is generally used when comparing the means of two separate groups where sample values from one population are not related to sample values from the other population. For example, comparing the mean weight loss of individuals on a powder diet versus a liquid diet, with independent groups of individuals, would involve an independent groups t-test (given that population standard deviations or variances are unknown).
Comparing two means from matched pairs, on the other hand, deals with dependent samples, where two measurements are drawn from the same pair of individuals or objects, such as before-and-after measurements in a single group. So, the correct answer to how an independent-measures design is different is option A: in an independent-measures design, there are two independent samples that are compared to one another.