Ch. 4 stats
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variability
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= n1

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
same