What is outlier masking as defined by Barnett and Lewis classic "Outliers in Statistical Data"?
Are there any simple or good real-world examples of outlier masking to help elucidate it?
Further, what outlier detection methods are resistant versus susceptible to outlier masking? As an example of this, does Grubb’s test compensate against outlier masking by its iterative approach?
Edit: from the article linked by @Saurabh-Gupta is the following definition of the masking effect (originally from Acuna and Rodriguez (2004)).
Masking effect. It is said that one outlier masks a second outlier, if the second outlier can be considered as an outlier only by itself, but not in the presence of the first outlier. Thus, after the deletion of the first outlier the second instance is emerged as an outlier. Masking occurs when a cluster of outlying observations skews the mean and the covariance estimates toward it, and the resulting distance of the outlying point from the mean is small.
This shows the rationale for the Grubb’s test being iterative, and indeed an example of the value of iterative methods. The value of @Dave’s answer is more subtle. It is not strictly a masking effect by the above definition, but it shows that the standard deviation’s standard error can be large in some situations and this could (for some samples) produce the same effect of masking.
From the same paper (and again originally from Acuna and Rodriguez (2004)), an example of where outliers are “created” from other outliers:
Swamping effect. It is said that one outlier swamps a second observation, if the latter can be considered as an outlier only under the presence of the first one. In other words, after the deletion of the first outlier the second observation becomes a non-outlying observation. Swamping occurs when a group of outlying instances skews the mean and the covariance estimates toward it and away from other non-outlying instances, and the resulting distance from these instances to the mean is large, making them look like outliers