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 normal distribution, approximately 99.7% of scores fall within three standard deviations of the mean, according to the empirical rule. This means that only about 0.3% of scores lie beyond three standard deviations from the mean—0.15% in each tail. Thus, scores more than three standard deviations above or below the mean are quite rare.
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.
In a normal standard curve, approximately 68% of scores fall within one standard deviation from the mean. This is part of the empirical rule, which states that about 95% of scores lie within two standard deviations, and about 99.7% fall within three standard deviations. Thus, the majority of data points are clustered around the mean.
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.