If this is not possible, you may wish to investigate it's effect on any statistical analysis. For example, outliers on the x axis of a regression analysis may have a disproportionate effect on the slope and intercept of that line. Typically, you should carry out the analysis with and without the outliers present. These differences are called residuals . In general residuals are thought to be a consequence of experimental error .
Many statistical analyses make assumptions about the residuals.
If you make a prediction about the category to which a case belongs you may get this prediction correct or incorrect. The accuracy of your predictions are summarised in a confusion matrix.