provides a quantitative measure of the differences between scores in a distribution and describes the degree to which the scores are spread out or clustered together
deviation
the distance from the mean
population variance
equals the mean squared deviation. variance is the average squared distance from the mean
standard deviation=
standard deviation= √ varience
ss or sum of squared
is the sum of the squared deviation scores
degrees of freedom or df
is determined by the number of scores in the sample that are independent and free to vary
- defined as df= n-1
unbiased sample statistic
if the average value of the statistic is equal to the population parameter
biased sample statistic
is the average value of the statistic either underestimates or overestimates the corresponding population parameter
standard deviation is primarily a descriptive measure: it describes
how variable or how spread out the scores are in the distribution
Adding a constant to each score (does or does not) change the standard deviation
does not
multiplying each score by a constant causes the standard deviation to be multiplied by the (same or different) constant