MSD Manual

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Does Everyone Need a Test?

Does Everyone Need a Test?

In short, no. Although many people find medical tests reassuring, test results are not always right:

  • Results are sometimes normal in people who have a disease (false negative).

  • Results are sometimes abnormal in people who do not have a disease (false positive).

To test or not to test? The possibility of getting a false-positive result can make testing a poor idea. When the probability that someone has a disease is lower than the probability that the test for that disease will be falsely positive, the test is likely to be misleading.

An example: Suppose parents are concerned that their 4-year-old daughter might have a urinary tract infection (UTI) because she is walking holding her thighs together. However, in the office, the doctor discovers that the girl has no other signs that suggest she has a UTI. That is, the girl is not urinating more frequently, she does not have pain or burning with urination, and her bladder and kidneys are not tender. Based on these findings, the doctor concludes that the likelihood of a UTI is very low (no more than 5%) and reassures the parents that nothing needs to be done unless other symptoms develop. The parents say they would feel better if the doctor did a urine test to prove their daughter did not have a UTI. Would a test help or hurt?

Evaluating the potential usefulness of test results: Suppose the doctor did a test for UTI that was known to give false-positive results 10% of the time (10% false positive is typical for many medical tests).

Even assuming that the test was always positive when people did have a UTI, that means that in 100 little girls like this one

  • The 5 who actually had a UTI would have a truly positive test.


  • 10 would have a falsely positive test.

In other words, in this particular little girl, a positive test result is twice as likely to be wrong as it is to be right.

Impact of test results on decision making: Thus, in this case, even a positive test result should not change the doctor’s decision not to treat, because that positive test result is likely to be wrong. Because the doctor would not do anything different, it makes no sense to do the test in the first place.

It would be a different story if the doctor thought the likelihood of a UTI was higher. If the likelihood were 50-50, most of the people with a positive test result would actually have a UTI, and testing would be helpful.

This math helps explain why doctors try to do tests only when there is a reasonable probability that people have the disease for which they are being tested.