Logit models have been widely used in marketing to predict brand choice and to make inference about the impact of marketing mix variables on these choices. Most researchers have followed the pioneering example of Guadagni and Little, building choice models and drawing inference conditional on the assumption that the logit model is the correct speci"cation for household purchase behaviour. To the extent that logit models fail to adequately describe household purchase behaviour, statistical inferences from them may be #awed. More importantly, marketing decisions based on these models may be incorrect.This research applies White's robust inference method to logit brand choice models. The method does not impose the restrictive assumption that the assumed logit model speci"cation be true. A sandwich estimator of the covariance &corrected' for possible mis-speci"cation is the basis for inference about logit model parameters. An important feature of this method is that it yields correct standard errors for the marketing mix parameter estimates even if the assumed logit model speci"cation is not correct.Empirical examples include using household panel data sets from three di!erent product categories to estimate logit models of brand choice. The standard errors obtained using traditional methods are compared with those obtained by White's robust method. The "ndings illustrate that incorrectly assuming the logit model to be true typically yields standard errors which are biased downward by 10}40 per cent. Conditions under which the bias is particularly severe are explored. Under these conditions, the robust approach is recommended.