In the past, methods for rejection of outliers have been investigated mostly without regard to the quantitative consequences for subsequent estimation or testing procedures. Moreover, although rejection of outliers with subsequent application of least squares methods is one of the oldest and most widespread classes of robust procedures, until recently no comparison was made with other robust methods. In this article the simplest situation, namely estimation of a location parameter in the potential presence of outliers, is treated by means of a Monte Carlo study. This study yields Monte Carlo variances of the “arithmetic mean” after rejection of outliers according to several classical and recent formal rules. The results are also compared with those for other robust estimators of location parameters. It turns out that a simple summary and theoretical explanation of the Monte Carlo results is provided by the breakdown points of the combined rejection-estimation procedures. As a by-product, the concept of breakdown point also leads to a better understanding of the so-called “masking effect” and can in fact replace the latter concept. Formulas for the breakdown points are given for the six types of rejection rules used. Some general aspects and properties of all methods for the rejection of outliers and their relation to other robust methods are also discussed. Finally, the treatment of outliers in the context of real data is considered, and several examples are briefly mentioned; one real-life example is analyzed in greater detail.