False Positives and False Negatives

Test Says “Yes” … or does it?

When you have a test that can say “Yes” or “No” (such as a medical test), you have to think:

  • It could be wrong when it says “Yes”.
  • It could be wrong when it says “No”.


It is like being told you did something when you didn’t!Or you didn’t do it when you really did.

There are special names for this, called “False Positive” and “False Negative”:

They say you did They say you didn’t
You really did They are right! “False Negative”
You really didn’t “False Positive” They are right!

Here are some examples of “false positives” and “false negatives”:

  • Airport Security: a “false positive” is when ordinary items such as keys or coins get mistaken for weapons (machine goes “beep”)
  • Quality Control: a “false positive” is when a good quality item gets rejected, and a “false negative” is when a poor quality item gets accepted
  • Antivirus software: a “false positive” is when a normal file is thought to be a virus
  • Medical screening: low-cost tests given to a large group can give many false positives (saying you have a disease when you don’t), and then ask you to get more accurate tests.

But many people don’t understand the true numbers behind “Yes” or “No”, like in this example:


Example: Allergy or Not?

Hunter says she is itchy. There is a test for Allergy to Cats, but this test is not always right:

  • For people that really do have the allergy, the test says “Yes” 80% of the time
  • For people that do not have the allergy, the test says “Yes” 10% of the time (“false positive”)

Here it is in a table:

Test says “Yes” Test says “No”
Have allergy 80% 20% “False Negative”
Don’t have it 10% “False Positive” 90%


Question: If 1% of the population have the allergy, and Hunter’s test says “Yes”, what are the chances that Hunter really has the allergy?

Do you think 75%? Or maybe 50%?

A test similar to this was given to Doctors and most guessed around 75% …
… but they were very wrong!

(Source: “Probabilistic reasoning in clinical medicine: Problems and opportunities” by David M. Eddy 1982, which this example is based on)

There are two good ways to work this out: “Imagine a 1000” and “Tree Diagrams”.


Try Imagining A Thousand People

When trying to understand questions like this, just imagine a large group (say 1000) and play with the numbers:

  • Of 1000 people, only 10 really have the allergy (1% of 1000 is 10)
  • The test is 80% right for people who have the allergy, so it will get 8 of those 10 right.
  • But 990 do not have the allergy, and the test will say “Yes” to 10% of them,
    which is 99 people it says “Yes” to wrongly (false positive)
  • So out of 1000 people the test says “Yes” to (8+99) = 107 people

As a table:

1% have it Test says “Yes” Test says “No”
Have allergy 10 8 2
Don’t have it 990 99 891
1000 107 893

So 107 people get a “Yes” but only 8 of those really have the allergy:

8 / 107 = about 7%

So, even though Hunter’s test said “Yes”, it is still only 7% likely that Hunter has a Cat Allergy.


As A Tree

Drawing a tree diagram can really help:

First of all, let’s check that all the percentages add up:

0.8% + 0.2% + 9.9% + 89.1% = 100% (good!)

And the two “Yes” answers add up to 0.8% + 9.9% = 10.7%, but only 0.8% are correct.

0.8/10.7 = 7% (same answer as above)


When dealing with false positives and false negatives (or other tricky probability questions) it pays to:

  • Imagine you have 1,000 (of whatever)
  • Or make a tree diagram

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