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These outliers will result in averages that are not representative of the sample. In mostcases, services that compute and report average values for multiples either throw out theseoutliers when computing the averages or constrain the multiples to be less than or equal toa fixed number. For instance, any firm that has a price earnings ratio greater than 500 maybe given a price earnings ratio of 500.When using averages obtained from a service, it is important that you know howthe service dealt with outliers in computing the averages. In fact, the sensitivity of theestimated average to outliers is another reason for looking at the median values formultiples.Biases in Estimating MultiplesWith every multiple, there are firms for which the multiple cannot be computed.Consider again the price-earnings ratio. When the earnings per share are negative, theprice earnings ratio for a firm is not meaningful and is usually not reported. When lookingat the average price earnings ratio across a group of firms, the firms with negative earningswill all drop out of the sample because the price earnings ratio cannot be computed. Whyshould this matter when the sample is large? The fact that the firms that are taken out ofthe sample are the firms losing money creates a bias in the selection process. In fact, theaverage PE ratio for the group will be biased upwards because of the elimination of thesefirms.There are three solutions to this problem. The first is to be aware of the bias andbuild it into the analysis. In practical terms, this will mean adjusting the average PE downto reflect the elimination of the money-losing firms. The second is to aggregate the marketvalue of equity and net income (or loss) for all of the firms in the group, including the