sample mean:
When most people use the word average, they are talking about the mean.
where
is the sample meanis the sumis the data valuesis the sample size
The mean is the sum of the data values divided by the sample size.Important characteristics of the mean are below:is sensitive to extreme scoresis
not necessarily a possible value
sample median:
the middle scoreProcedure for calculating (denotes the sample median) follows: rank data from smallest to largest, if n is odd,
median is the middle score, if n is even, median is the average of two middle scores, Important characteristics of the median are below: is not
sensitive to extreme scores, exactly half of the data is below
and exactly half of the data
is above Because of the characteristics of the mean and
median, if extreme scores exist in a data set the median is a better measure of central tendency.If extreme scores are unlikely, the mean varies less
from sample to sample than the median and is a better measure.
sample mode:
the most frequent score. There are some major weaknesses with the mode. Notice changing one value totally changes the mode
making it unstable. The one advantage of using the mode is that it can be used for qualitative data and that is when it should be used. The mean or
median cannot be used for qualitative data.
A measure of dispersion is a measure of the spread of the data points or we can also say a measure of how much variability there is in the data set.
The most common way to numerically describe a data set is with a measure of center and a measure of dispersion.
sample range:
Represents the distance between the high and low value. range = high score  low score. Important characteristics of the range are
below: easy to compute, totally sensitive to extreme scores, not a good measure of dispersion in most cases
sample variance:
measures the average squared distance the data values are from
whereis the sample varianceis the sum is the data values is the
sample meanis the sample size
Notice there are two ways to calculate . The first way
is the conceptual formula and you can see that this equation comes straight from the
definition. The second equation
is the algebraic equivalent of the first and is the one I suggest you use when calculating variance
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 Fall '10
 Bradley,W
 Statistics, Normal Distribution, Standard Deviation, Variance

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