Here, you can see a unit square which represents a Bayesian situation in which 1000 people are tested with a medical diagnostic test. In the beginning it is assumed:

8% of all people are ill.

90% of the ill people are identified with the medical diagnostic test and hence test positive.

15% of the healthy people are tested positive by mistake.

An interesting question in such a situation is: How likely is a person actually ill, if this person tests positive?
This probability is equivalent to the proportion of ill (and positively tested) people among all positively tested people and is represented in the visual fraction with the appropriate areas from the unit square.

The given information in the Bayesian situation can vary. Hence, the question arises, how variations of the three given pieces of information affect the probability that a person is actually ill, if this person tests positive.

With this simulation you can visualize and analyse the effects of such variations in the unit square and the visual fraction, which represents the probability of interest.