What is the term used for the degree to which scores in a distribution are spread out or dispersed?

​Measures of distribution include:

• Range
• Variance 
• Standard deviation ​

Two data set can have similar means but may have differences in dispersion.  
For example: Data set A & B

Range is the simplest measure of dispersion. Determination of range is based on only two values in a data set ( highest value and lowest value) and is easy to be computed. 

A large range indicates a more dispersed data set about the mean while a small range exhibits a more clustered data about its mean. 
Range is very sensitive to outlier. In its calculation, range ignores all values in the data set except the two values( highest and lowest values). In the case that the highest and lowest values are unsusually extremes, value of range will not be valid. 

Variance

The variance is actually the average of the square of the distance that each value is from the mean. Therefore, if the values are near the mean, the variance will be small. In contrast, if the values are far from the mean, the variance will be large. 
"The higher variance indicates the greater variability and the smaller varience indicates  the lower variability"

Standard Deviation

• Standard deviation is the most important and frequently used measure of dispersion. 
• Standard deviation which is denoted by S is the positive square root of the variance; and vice versa, squaring the standard deviation gives the variance. 

• Standard deviation indicates how far the individual responses to a question vary or deviate from the mean. 
• Standard deviation tells the researcher how spread out the responses are, are they concentrated around the mean or scattered far and wide.
• The variance and standard deviation of a data set can never be negative. 

The standard deviation is the average amount by which scores differ from the mean. The standard deviation is the square root of the variance, and it is a useful measure of variability when the distribution is normal or approximately normal (see below on the normality of distributions). The proportion of the distribution within a given number of standard deviations (or distance) from the mean can be calculated.

A small standard deviation coefficient indicates a small degree of variability (that is, scores are close together); larger standard deviation coefficients indicate large variability (that is, scores are far apart).

The formula to calculate the standard deviation is

Note that the standard deviation is the square root of the variance.

Example: how to calculate the standard deviation:

In the previous section- Variance- we computed the variance of scores on a Statistics test by calculating the distance from the mean for each score,t hen squaring each deviation from the mean, and finally calculating the mean of the squared deviations.

Since we already know the variance, we can use it to calculate the standard deviation. To do so, take the square root of the variance. The square root of 1.5 is 1.22. The standard deviation is 1.22.

Distributions with the same mean can have different standard deviations. As mentioned before, a small standard deviation coefficient indicates that scores are close together, whilst a large standard deviation coefficient indicates that scores are far apart. In this example, both sets of data have the same mean, but the standard deviation coefficient is different:

In this example, the scores in Set A are 0.82 away from the mean; in Set B, scores are 2.65 away from the mean, even though the mean is the same for both sets. So scores in Set B are more dispersed than scores in Set A.