Fisher argues against inverse probability as a basis for statistical inferences, and instead proposes inferences based on likelihood functions.

However, judgments by representativeness only look at the resemblance between the hypothesis and the data, thus inverse probabilities are equated:

"It's the law of inverse probabilities," an engineer said.

The two diagrams partition the same outcomes by A and B in opposite orders, to obtain the inverse probabilities.

Fisher rejected the Bayesian view, writing that "the theory of inverse probability is founded upon an error, and must be wholly rejected".

A two parameter Benini variable can be generated by the inverse probability transform method.

Fiducial inference can be interpreted as an attempt to perform inverse probability without calling on prior probability distributions.

In probability theory, inverse probability is an obsolete term for the probability distribution of an unobserved variable.

In a generative approach, however, the inverse probability is instead estimated and combined with the prior probability using Bayes' rule, as follows:

After the 1920s, "inverse probability" was largely supplanted by a collection of methods that came to be called frequentist statistics.