In statistical studies, conclusions drawn from the sample can only be justified if the sample is what?

In statistical studies, the validity of conclusions drawn from a sample hinges on the sample being representative of the population from which it is drawn. A representative sample accurately reflects the characteristics and diversity of the entire population, ensuring that the findings can be generalized beyond the sample itself. If a sample is not representative, any conclusions made may not hold true for the wider population, leading to biased or misleading results.

While having a large sample size can enhance the reliability of results and reduce sampling error, it does not guarantee representativeness. Similarly, a random sample is important for minimizing bias in selecting participants, but if the sample does not represent the population's diversity and key characteristics, the conclusions still risk being inaccurate. Controlled samples are relevant in experimental designs for establishing cause-and-effect relationships, but they also do not inherently ensure that the samples are representative of a larger population.

Thus, the key factor that justifies conclusions in statistical studies is the representativeness of the sample.