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VAR(X), the squared standard error of the sample mean, with s 2 /n, where s 2 is the sample variance based on the n observations left after outliers are discarded However, there are two concerns with this approach First, it results in using the wrong standard error; second, discarding outliers in some manner is often met with incredulity because it seems counterintuitive based on what has become traditional training in statistics In particular, it might seem that this must result in less accurate results and less power First consider the issue of accuracy and power when some of the smallest and largest observations are discarded To take an extreme case, consider the usual sample median, which discards all but the middle one or two values As illustrated in Figure 32, it can be more accurate on average versus the mean, as was rst noted by Laplace in 1775.



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By 1818 Laplace was aware of more general conditions under which the median beats the mean in accuracy To provide some sense of why this occurs, imagine that 20 observations are randomly sampled from a standard normal distribution Now put these values in ascending order and label the results X (1) X (20) It can be shown that with probability 983, the smallest value will be less than 09 That is, P(X (1) 09) = 983 Similarly, P(X (20) 09) = 983 That is, there is a high probability that the smallest and largest observations will be relatively far from the population mean, the value we are trying to estimate.

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Of course, averaging these values gives a reasonable estimate of the population mean, but the point is that in general we would expect them to add a relatively large amount of variation versus the two middle values, which have a much higher probability of being close to the population mean But as is well known, despite this property, the sample mean performs much better than does the median under normality The concern, however, is that for nonnormal distributions there are situations where the opposite is true Why does the mean beat the median in accuracy under normality The answer is that when we put the observations in order, they are no longer independent, and the correlation among the ordered observations is such that under normality the mean beats the median.

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To elaborate a bit, consider three observations randomly sampled from a normal distribution: X 1 , X 2 , and X 3 Then each has probability 05 of being less than or equal to 1645 But suppose we put the observations in ascending order, yielding X (1) X (2) X (3) Thus, X (1) is the smallest of the three observations, and X (3) is the largest To see why these three variables are no longer independent, rst note that there is some positive probability that X (2) is less than 1645 If X (1) is independent of X (2) , then knowing the value of X (1) should not alter the probabilities associated with X (2) But given that X (1) is greater than 1645, for.

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