The 538 article actually makes a lot of sense and is a pretty decent introduction to a common public health issue.<p>&gt; Their assertion that tests === diagnostics that map 1:1 to a disease was baseless. Tests don&#x27;t answer the question &quot;doe patient have diabetes?&quot;, they answer the question &quot;what is the patients glucose level?&quot; 115 mg&#x2F;dl.<p>Medical tests that aren&#x27;t actionable aren&#x27;t useful. So while you&#x27;re correct in a very narrow sense, you have to use the results to make a decision about treatment. If you&#x27;re going to make treatment decisions based on tests that have some amount of inherent error, you may end up over or under-treating patients.<p>&gt; Maybe the patient forgot to fast, maybe the test was a fluke? Either way a consistent average of 85 would put that out of range.<p>Medical tests have both <i>random</i> and <i>systematic</i> error. What you&#x27;re talking about is <i>random</i> error (i.e. a single test result being bad). However, many tests have systematic error (i.e. the result being wrong <i>every time the test is taken</i>) just due to personal variation in what&#x27;s &quot;normal&quot;.<p>The article makes the point that if more tests lead to worse outcomes[1] (due to overtreatment) then adding more tests is useless or worse than useless. This effect is particularly pronounced when testing across large populations, even if the test is highly accurate.<p>[1] And in many cases they do. For example, early mammogram screenings result in <i>worse</i> outcomes for patients when done across a large population. See <a href="http:&#x2F;&#x2F;annals.org&#x2F;article.aspx?articleid=2480757" rel="nofollow">http:&#x2F;&#x2F;annals.org&#x2F;article.aspx?articleid=2480757</a>