SOC 3142 Lecture Notes - Lecture 6: Squared Deviations From The Mean, Central Tendency, Standard Deviation
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The mean (x, ex bar) is the most common measure of central tendency. Where: sum (upper case sigma, summation sign) xi (ex sub i, all the scores) Or: mean equals summation of all scores, divided by the number of cases. The mean has a number of mathematical properties useful to statistics. The mean is the point in any distribution where all of the scores (xi) cancel out: The sum of all the differences from the mean always adds up to zero. The mean is a good measure of the centrality of the distribution: it exactly balances out the distribution. The mean and the sum of squares: The sum of the square of the differences between each score and the mean (known as the sum of squared deviations, ss) is the smallest possible difference in the distribution. This property indicates that the mean is closer to all of the scores than any other measure of central tendency.