Let’s say Avian Flu breaks out in your country. The government discover only 1 in 10,000 people are affected by it but you visit the doctors to be tested just in case, after all it is deadly. Unfortunately the results come back, you discover the results are positive and you collapse in horror.

but should you really be worried?

Assume 1 million people take the test across the country. Only 100 people will be infected and 99 of them will be correctly diagnosed.

999,900 people are not infected but because the test is only correct 99% of the time, 9,999 will be incorrectly told they tested positive. Despite your result being positive, statistically you are more likely to be one of the 9,999 that received false positives than the 99 that are correctly tested as positive.

This leaves one person that is carrying Avian Flu but told incorrectly they are clear.

1,000,000 Take the test
999,900 Are not infected.
9,999 are incorrectly given a positive
100 are infected
99 will be found
1 is given an incorrect negative.

In this scenario you’re over 100 times more likely to be clear despite a positive result from the test.

If you scale up the figures to represent a medium sized country;

60,000,000 Take the test
59,994,000 Are not infected.
599,940 are incorrectly given a positive
6000 are infected
5940 will be found
60 are given an incorrect negative.

Are you one of the people getting an incorrect positive in your tests?

The figures for individual medical tests (i.e. HIV, Cancer, pregnancy) will vary depending on infection rate and number of people taking the test.

Update: I definitely do not think Pregnancy is an infection. I chose my words poorly!