Here, we denote by the event NOT
.
Example 1: Squirrel Flu Testing
In a forest, a group of squirrels is concerned about a new illness called “Squirrel Flu.” It’s more dangerous than the ordinary flu. From the data collected, they know that
- Probability of a squirrel having Squirrel Flu
- Probability of testing positive if a squirrel has Squirrel Flu
- Probability of testing positive if a squirrel does not have Squirrel Flu
Now, A squirrel named Nutty takes the test and it comes back positive. What is the probability that Nutty actually has Squirrel Flu?
Answer:
Using Bayes’ Theorem:
Now:
Hence,
So, Nutty has about a 33.3% chance of actually having Squirrel Flu upon testing positive.
Example 2: Deer Allergy Testing
In the forest, some deer have allergies to a particular type of flower.
- Probability of a deer having allergies
- Probability of the test detecting allergies if the deer has them
- Probability of the test being positive if the deer does not have allergies
Now, a deer named Nambi tests positive for allergies. What is the probability that Nambi actually has allergies?
Using Bayes’ Theorem:
Now:
So,
So, Nambi has about a 75% chance of actually having allergies upon testing positive.
These examples show how conditional probability can help forest creatures interpret their medical test results.