Models are powerful tools if their outputs are relevant!
Therefore, knowing the reliability of models is essential for people who wish to use them, as well as for researchers who attempt to improve them. Whatever the nature of the model output, objective evaluation consists of comparing predicted or calculated events with observed events.
Such comparison can only focus on available samples of observed events. Obviously, the results depend on the choice of the sample. However, inferential statistics enable one to extend results obtained from a random sample to general use.
An unbiased method of testing boolean avalanche-prediction models is suggested: the validity of this type of model should be characterized by the probability that the proportion of correct forecasts is within a given confidence interval. This interval is calculated from the sample size, according to the Gaussian table.
This unrestricted principle can be used to prove all kinds of static models, if ever their outputs are verifiable, enabling one to calculate the ratios of correct forecasts as well as the ratios of well-predicted events and can also be extended to verify probabilistic predictions.