Performance persistence studies typically suffer from ex-post conditioning biases. As stressed by Carhart (1997a) and Carpenter and Lynch (1999), standard methods of analysis on a survivorship free sample are subject to look-ahead biases. In this paper, we show how one can easily correct for look-ahead bias using weights based on probit regressions.First, we model how survival probabilities depend upon historical returns, fund age and aggregate economy-wide shocks, using two samples of US based 'income' and 'growth' funds. Subsequently, we employ a Monte Carlo study to analyze the size and shape of the look-ahead bias in performance persistence that arise when a survivorship free sample is used with standard techniques. In particular, we show that look-ahead bias induces a spurious U-shaped pattern in performance persistence. Finally, we demonstrate how a weighting procedure based upon probit regressions can be used to correct for this bias. In this way, we obtain look-ahead bias-corrected estimates of abnormal performance relative to a one-factor and the Carhart (1997b) four-factor model, as well as its persistence. The results suggest that in this sample, look-ahead bias is of minor importance and does not seriously affect estimates of persistence. Our bias-corrected results closely correspond to the findings of Carhart (1997b), implying that there is no evidence on a risk-adjusted basis for persistence in performance.