They are observations with a low likelihood of occurrence. They may be called outliers but there is no agreed definition for outliers.
Outliers.
In a normally distributed data set, approximately 95% of the data falls within two standard deviations of the mean. This is part of the empirical rule, which states that about 68% of the data falls within one standard deviation and about 99.7% falls within three standard deviations. Therefore, two standard deviations capture a significant majority of the data points.
Three standard deviations refer to a statistical measure that indicates the range within which approximately 99.7% of data points in a normal distribution fall. In other words, if you have a dataset with a mean (average) value and a standard deviation, three standard deviations above and below the mean encompass nearly all the data points, highlighting the spread and variability of the data. This concept is often used in quality control and statistics to identify outliers or extreme values.
Nearly all the values in a sample from a normal population will lie within three standard deviations of the mean. Please see the link.
Measures of the general value are a common need. Average, Median, and Mode are the three commonest.Average is the arithmetic average of all the values.Median is the actual measurement which is midwaybetween the extreme values, and is often closest to the average.Mode is the commonest value.Other indicators of central tendency, may ignore all value beyond say, three standard deviations, and thus ignore the contribution by the extreme, and uncommon, values.
Outliers.
It is one of the informal definitions for an outlier.
outliers
Usually they would be observations with very low probabilities of occurrence.
I believe outliers is the best answer to this question. The previous answer was average, which is the mean.
variances
Outliers
Extreme values. They might also be called outliers but there is no agreed definition for the term "outlier".
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.
It is a measurement which may, sometimes, be called an extreme observation or an outlier. However, there is no agreed definition for outliers.
The answer depends on what the standard deviation is.
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.