From this information you can assess whether individual determinations
in your project are likely to be outliers given all of
the information supplied (in the model, prior and data). In general,
if the prior probability of being an outlier is higher than
the posterior probability, that particular determination is less likely
to be an outlier then you originally thought. On the
other hand, if the posterior probability of being an outlier is higher
than the prior probability, the determination is more
likely to be an outlier then you originally thought.
Being able to compare the prior and posterior probabilities takes some
practice, but it is vital that you resist the temptation
to experiment with a range of prior probabilities. Prior means
just what it suggests. You must assess your prior
probabilities that samples are outliers on the basis of information
available to you BEFORE you obtained the
determinations from the laboratory (factors like `old wood', possible
contamination, etc.). You should never use this option
simply on the basis that you don't like the look of some of the determinations
you obtain!