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This article introduces basic features of error estimators, including bias, variance, and loss functions. It outlines the logic behind classical hypothesis tests and explains the special challenges faced by knowledge discovery algorithms that search large model spaces. It discusses the statistical effects of multiple comparison procedures (MCPs), and several methods to adjust for those effects, including mathematical adjustments, cross-validation, and randomization tests. Finally, it outlines the basic concepts behind overfitting reduction and pruning.