The least-squares algorithm is known to bias apparent diffusion coefficient (ADC) values estimated from magnitude MR data, although this effect is commonly assumed to be negligible. In this study the effect of this bias on tumor ADC estimates was evaluated in vivo and was shown to introduce a consistent and significant underestimation of ADC, relative to those given by a robust maximum likelihood approach (on average, a 23.4 +/- 12% underestimation). Monte Carlo simulations revealed that the magnitude of the bias increased with ADC and decreasing signal-to-noise ratio (SNR). In vivo, this resulted in a much-reduced ability to resolve necrotic regions from surrounding viable tumor tissue compared with a maximum likelihood approach. This has significant implications for the evaluation of diffusion MR data in vivo, in particular in heterogeneous tumor tissue, when evaluating bi- and multiexponential tumor diffusion models for the modeling of data acquired with larger b-values (b > 1000 s/mm(2)) and for data with modest SNR. Use of a robust approach to modeling magnitude MR data from tumors is therefore recommended over the least-squares approach when evaluating data from heterogeneous tumors.