Statistics. Prior Probability: What Is It?
Prior probability is the estimated probability of the state of affairs before a practical survey is conducted to acquire relevant details. This is usually a conclusion based on past events. For instance, Peter passed the test (A) last year; therefore, the probability P (Pc) he will pass test C is 0.85. This probability is entirely hypothetical unless it is finally linked to evidence that supports its calculation. Some of the events from which prior probability is derived are rare incidents and temporary factors. If a soccer team has won the last five matches, then a fan may predict a win for the team in the next match. Nonetheless, the results of the match have to be used to verify the hypothesis. The variation between the prediction and the match results is decidedly vital.
The accuracy of prior probability is determined by how closer it is to the practical evidence. There are some uncertainties that make prior probability inaccurate. For example, vote results may be affected by rigging. The connection between the hypothesis and the evidence essentially aids in the management of any ambiguity that may be encountered. If the values of probabilities are derived from data provided by an experienced statistician, then the values are ideally easy to understand. The sample set for the data collected should be wide in order to cover all possible factors affecting the probabilities. Bayes advanced a theorem that could be used in predicting prior probability.