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Generally, significance tests can be classified into parametric and nonparametric tests. The literature on event-study hypthesis testing covers a wide range of tests and is thus very comprehensive. \begin$ denotes the forecast-error-corrected variance. Formally, the testing framework reads as follows: The answer about statistical significance is given by means of hypothesis testing, where the null hypothesis ($H_0$) states that the mean of the abnormal returns within the event window is zero and the alternative hypothesis ($H_1$) states the opposite. In the relevant literature, the focus is almost always on the mean of the distrubtion of abnormal returns and, specifically, one seeks to answer the questions whether this mean is different from zero (with statistical significance). Either they are used as dependent variables in subsequent regression analyses or they are interpreted as such. This latter direct interpretation seeks to answer the question whether the distribution of the abnormal returns is systematically different from predicted. The abnormal and cumulative abnormal returns from event studies are typically used in two ways.