You cannot have a standard deviation for 1 number.
Z-Score tells how many standard deviations a measurement is away from the mean.
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 1.28
The sum of standard deviations from the mean is the error.
95% is within 2 standard deviations of the mean.
2.576 sd
How many standard deviations is 16.50 from the mean?
Z-Score tells how many standard deviations a measurement is away from the mean.
That depends on what the standard deviation is.
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.
The z-score of a value indicates how many standard deviations it is from the mean. If a value is 2.08 standard deviations greater than the mean, its z-score is simply 2.08. This means the value lies 2.08 standard deviations above the average of the dataset.
the Z score, or standard score.
It is 1.28
95 percent of measurements are less than 2 standard deviations away from the mean, assuming a normal distribution.
The sum of standard deviations from the mean is the error.
z score
95% is within 2 standard deviations of the mean.