How do I calculate and interpret conditional probabilities?

Gigerenzer (2002) suggests a way to obtain conditional probabilities using frequencies in a decision tree.

Cortina and Dunlap (1997) give an example evaluating the detection rate of a test (positive/negative result) to detect schizophrenia (disorder).

To do this one fixes the following:

The base rate of schizophrenia in adults (2%)

The test will correctly identify schizophrenia (give a positive result) on 95% of people with schizophrenia

The test will correctly identify normal individuals (give a negative result) on 97% of normal people.

Despite this we can show the [attachment:bayes.doc test is unreliable].

This is a more intuitive way of illustrating the equivalent Bayesian equation:

$$\mbox{P(No disorder|+ result) = }\frac{\mbox{P(No disorder) * P(+ result | No disorder)}}{\mbox{P(No disorder) * P(+ result | No disorder) + P(Disorder) * P(- result | Disorder)}}$$

A talk with subtitles further illustrating aspects of conditional probabilities given by Ted Donnelly (Oxford), a geneticist, is available for viewing [http://blog.ted.com/2006/11/statistician_pe.php here.]

• [attachment:bayes2.doc:More on Bayes theorem:Illustration of priors and likelihoods]

References

Cortina JM, Dunlap WP (1997) On the logic and purpose of significance testing Psychological methods 2(2) 161-172.

Gigerenzer G. (2002) Reckoning with risk: learning to live with uncertainty. London: Penguin.