The target statistical concept is diagnostic and screening tests and the “gold standard” test in the sphere of healthcare. The “gold standard” test is usually the preferred version of diagnosing a patient with a specific disease. However, it is typically expensive or difficult to access and distribute (Ancker and Begg 5). Thus, one may choose to perform cheaper, simpler, but less specific screening tests instead. Here, the decision lies in choosing between the sensitivity and specificity of the diagnostics that are not the “gold standard.”
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An analog is a scenario where a person wants to cook a batch of spinach. They like spinach leaves, but do not want to eat the stems. Therefore, a question arises of how one can cut a bunch of spinach to get as many leaves and few stems as possible. If the person decides to cut as many stems off, they may also cut some leaves, thus losing the amount of spinach they like. In contrast, if one chooses to preserve as many leaves as possible and cut lower, they may also have to eat some stems in the end (Ancker and Begg 5).
The knife in this analogy is the diagnostic test of choice. Its function is to separate patients with and without the disease. Spinach leaves are people without the disease, and the stems are those with the condition.
The selected test determines the knife’s position in separating two patient groups (Ancker and Begg 5). Statistical analyses, similar to the knife, cannot always accurately determine who has or does not have a disease.
One may argue that a cook can take each spinach leaf and cut the stem, increasing the accuracy. This assumption may lead a scientist to think that by using diagnostic tests on every person individually, they will come to a better result. It is vital to explain that such a nuanced approach is an analogy of a “gold standard” – time-consuming, expensive, and difficult (Ancker and Begg 6).
The analogy of cooking spinach can effectively demonstrate the choice statisticians have to make when sacrificing either sensitivity or specificity of results. High sensitivity is correctly diagnosed people with the disease, and high specificity is correctly diagnosed healthy people. This comparison shows that there might be no ideal option of a diagnostic test, and optimization often depends on the particular case.
Ancker, Jessica S., and Melissa D. Begg. “Using Visual Analogies to Teach Introductory Statistical Concepts.” Numeracy, vol. 10, no. 2, 2017, pp 1-12.
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