This paper investigates the impact of event-induced variance on the effectiveness of event-study methods in detecting whether the average abnormal return is zero. The authors simulate an event with stochastic effects on stock returns for 250 samples of 50 securities each. They find that when an event causes even minor increases in variance, the most commonly-used methods reject the null hypothesis of zero average abnormal return too frequently when it is true, although they are reasonably powerful when it is false. They demonstrate that a simple adjustment to the cross-sectional techniques produces appropriate rejection rates when the null is true and equally powerful tests when it is false. The study shows that traditional event-study methods, such as the traditional method and the standardized-residual method, often reject the null hypothesis too frequently when there is no abnormal performance, due to an underestimation of event-period variance. In contrast, the standardized cross-sectional test, which combines the standardized-residual and ordinary cross-sectional approaches, performs better in controlling the Type I error rate and maintaining power when there is abnormal performance. The authors propose a 'standardized cross-sectional' test that is easy to implement and is a hybrid of Patell's standardized-residual methodology and the ordinary cross-sectional approach. This test incorporates variance information from both the estimation and event periods and is closely related to the special case of Ball and Torous' (1988) estimator in which there is no event-day uncertainty. The test is shown to be robust to event-date clustering and performs well in controlling the Type I error rate and maintaining power when there is abnormal performance. The study concludes that the standardized cross-sectional test is a more reliable method for detecting abnormal returns in the presence of event-induced variance. It is particularly effective in controlling the Type I error rate and maintaining power when there is abnormal performance. The results suggest that traditional event-study methods may be too sensitive to event-induced variance and may lead to incorrect conclusions about the effects of events on stock returns. The standardized cross-sectional test provides a more accurate and reliable method for analyzing event studies in the presence of event-induced variance.This paper investigates the impact of event-induced variance on the effectiveness of event-study methods in detecting whether the average abnormal return is zero. The authors simulate an event with stochastic effects on stock returns for 250 samples of 50 securities each. They find that when an event causes even minor increases in variance, the most commonly-used methods reject the null hypothesis of zero average abnormal return too frequently when it is true, although they are reasonably powerful when it is false. They demonstrate that a simple adjustment to the cross-sectional techniques produces appropriate rejection rates when the null is true and equally powerful tests when it is false. The study shows that traditional event-study methods, such as the traditional method and the standardized-residual method, often reject the null hypothesis too frequently when there is no abnormal performance, due to an underestimation of event-period variance. In contrast, the standardized cross-sectional test, which combines the standardized-residual and ordinary cross-sectional approaches, performs better in controlling the Type I error rate and maintaining power when there is abnormal performance. The authors propose a 'standardized cross-sectional' test that is easy to implement and is a hybrid of Patell's standardized-residual methodology and the ordinary cross-sectional approach. This test incorporates variance information from both the estimation and event periods and is closely related to the special case of Ball and Torous' (1988) estimator in which there is no event-day uncertainty. The test is shown to be robust to event-date clustering and performs well in controlling the Type I error rate and maintaining power when there is abnormal performance. The study concludes that the standardized cross-sectional test is a more reliable method for detecting abnormal returns in the presence of event-induced variance. It is particularly effective in controlling the Type I error rate and maintaining power when there is abnormal performance. The results suggest that traditional event-study methods may be too sensitive to event-induced variance and may lead to incorrect conclusions about the effects of events on stock returns. The standardized cross-sectional test provides a more accurate and reliable method for analyzing event studies in the presence of event-induced variance.