Neuropsychologists frequently rely on a battery of neuropsychological tests which are normally distributed to determine impaired functioning. The statistical likelihood of Type I error in clinical decision-making is in part determined by the base rate of normative individuals obtaining atypical performance on neuropsychological tests. Base rates are most accurately obtained by co-normed measures, but this is rarely accomplished in neuropsychological testing. Several statistical methods have been proposed to estimate base rates for tests that are not co-normed. This study compared two statistical approaches (binomial and Monte Carlo models) used to estimate the base rates for flexible test batteries. The two approaches were compared against empirically derived base rates for a multitest co-normed battery of cognitive measures. Estimates were compared across a variety of conditions including age and different α levels (N =3,356). Monte Carlo R(2) estimates ranged from .980 to .997 across five different age groups, indicating a good fit. In contrast, the binomial model fit estimates ranged from 0.387 to 0.646. Results confirm that the binomial model is insufficient for estimating base rates because it does not take into account correlations among measures in a multitest battery. Although the Monte Carlo model produced more accurate results, minor biases occurred that are likely due to skewess and kurtosis of test variables. Implications for future research and applied practice are discussed.
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