Measurements. Just because a particular result lies far from the mean doesn't make it any different. If it's noticeably far from the "crowd" of all the other measurements, it can be called an outlier. An outlier isn't necessarily bad, just different. It should be examined in detail to see if there's something odd about it, but not discarded out of hand.
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You may be referring to the statistical term 'outlier(s)'. Also, there is a rule in statistics called the '68-95-99 Rule'. It states that in a normally distributed dataset approximately 68% of the observations will be within plus/minus one standard deviation of the mean, 95% within plus/minus two standard deviations, and 99% within plus/minus three standard deviations. So if your data follow the classic bell-shaped curve, roughly 1% of the measures should fall beyond three standard deviations of the mean.
Samples
The square of the standard deviation is called the variance. That is because the standard deviation is defined as the square root of the variance.
population
standard error