Provides a quantitative measure of the difference between scores in a distribution and describes the degree to which the scores are spread out or clustered together
The distance covered by the scores in a distribution, from the smallest score to the largest.
URL=Xmax and LRL=Xmin
The distance from the mean
Equals the mean squared deviation. Variance is the averaged squared distance from the mean
Sum of Squares
Or the sum of the squared deviation scores
Definitional Formula for SS
1. Find each deviation score
2. Square each deviation score
3. Add the squared deviations
Computational formula of SS
1. Square each score and then add the squared values
2. Find the sum of the scores
3. Then square this total
4. Divide by
Population Standard Deviation
SS for sample
Sample Standard Deviation
Degrees of Freedom
For a sample of n scores, the degrees of freedom, or df, for the sample variance are defined as .
The degrees of freedom determine the number of scores in the sample that are independent and free to vary.
A sample statistic is biased if the average value of the statistic either underestimates or overestimates the corresponding population parameter
A sample statistic is unbiased if the average value of the statistic is equal to the population parameter,(the average value of the statistic is obtained from all the possible samples for a specific sample size, n)