Sum of squares of deviations from the mean is small.
.The test has a mean, or average, standard score of 100 and a standard deviation of 16 (subtests have a mean of 50 and a standard deviation of 8). The standard deviation indicates how far above or below the norm the subject's score is.
3 (mean − median) / standard deviation.
graded potentials
If the minimum value is the minimum observed value then it indicates that the distribution goes below the minimum observed value.If the minimum value is the minimum defined for the distribution then it indicates thatthe data do not come from the proposed distribution,estimates for the mean or standard deviation are incorrect, oryou have got a sample which is atypical.
Relative Standard Deviation (RSD) is a measure of precision (not accuracy). RSD is sometimes called coefficient of variation (CV) and often is calculated as a percentage. s = standard deviation x = mean RSD = s/x, as a percentage, (s/x) *100 The RSD allows standard deviations of different measurements to be compared more meaningfully. For example, if one is measuring the concentration of two compounds A and B and the result is 0.5 (+/-) 0.4 ng/mL for compound A and 10 (+/-) 2 ng/mL for compound B, one may look at the standard deviation for compound A and say because it is lower (0.4 vs. 2) than for B, the measurement for A was more precise. Actually this is not the case. When the %RSD is used the new values for compound A and B are 0.5 (+/-) 80% and 10 (+/-) 20% respectively, therefore, the measurement for compound B is more precise.
No, if the standard deviation is small the data is less dispersed.
That there is quite a large amount of variation between the observations.
No. A small standard deviation with a large mean will yield points further from the mean than a large standard deviation of a small mean. Standard deviation is best thought of as spread or dispersion.
Standard Deviation tells you how spread out the set of scores are with respects to the mean. It measures the variability of the data. A small standard deviation implies that the data is close to the mean/average (+ or - a small range); the larger the standard deviation the more dispersed the data is from the mean.
When the sample size is small
The standard deviation is the standard deviation! Its calculation requires no assumption.
A large standard deviation means that the data were spread out. It is relative whether or not you consider a standard deviation to be "large" or not, but a larger standard deviation always means that the data is more spread out than a smaller one. For example, if the mean was 60, and the standard deviation was 1, then this is a small standard deviation. The data is not spread out and a score of 74 or 43 would be highly unlikely, almost impossible. However, if the mean was 60 and the standard deviation was 20, then this would be a large standard deviation. The data is spread out more and a score of 74 or 43 wouldn't be odd or unusual at all.
The standard deviation of the population. the standard deviation of the population.
The standard deviation is 0.
A small sample and a large standard deviation
Information is not sufficient to find mean deviation and standard deviation.
Standard deviation is the square root of the variance.