For a sample size between 10 and 19, which action is recommended when establishing reference intervals?

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Multiple Choice

For a sample size between 10 and 19, which action is recommended when establishing reference intervals?

Explanation:
Estimating reference intervals requires enough measurements from healthy individuals to accurately define how values vary in the population. With only 10–19 samples, the distribution is too imprecise to reliably determine the central 95% interval or the tail boundaries, so calculating a reference interval would be unreliable. Therefore, the best approach is to refrain from deriving new reference intervals from such a small dataset. In practice, you would either use an externally published reference interval and verify its applicability to your population, or wait until more samples can be collected to meet recommended guidelines. The other options fall short because reporting only the mean ignores the variability around it, using an external interval without validation may not fit the local population, and applying a parametric method on an undersized sample relies on assumptions that can be false when the data are so limited.

Estimating reference intervals requires enough measurements from healthy individuals to accurately define how values vary in the population. With only 10–19 samples, the distribution is too imprecise to reliably determine the central 95% interval or the tail boundaries, so calculating a reference interval would be unreliable. Therefore, the best approach is to refrain from deriving new reference intervals from such a small dataset. In practice, you would either use an externally published reference interval and verify its applicability to your population, or wait until more samples can be collected to meet recommended guidelines. The other options fall short because reporting only the mean ignores the variability around it, using an external interval without validation may not fit the local population, and applying a parametric method on an undersized sample relies on assumptions that can be false when the data are so limited.

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