March 25, 2005
Statistics Term of the Day: Skew
Skewness is simply a measure of the non-symmetry of your distribution; thus, we can add it to measures of central tendency and variability in describing our distributions of scores. When distributions are skewed, this means that scores within that distribution are piled up on one end (or tail) more than on the other.
Skew can be negative or positive. To remember direction of skew, think of the positive/negative number scale, with the "negative" being to the left, and "positive" being to the right. The "skew" part is actually the skinny part of the distribution, not the end where all the numbers pile up. Thus, this distribution (which is what you'd see when measuring personal income, or number of children per family) is positively skewed:
Whereas this distribution of scores (such as you might see on a very easy exam) is negatively skewed:
You'd think that direction of skew would be easy to remember, but my time spent tutoring and teaching entry-level statistics suggests otherwise. Remember SK = "skew" = "skinny part". Graphing your data will make skew evident, but most statistical packages also calculate a statistic that quantifies the amount of skew in your data. In a skewed distribution, the mean, median, and mode may differ markedly from one another, so understanding the skew is crucial when describing your data (and in performing inferential stastistics, as we'll discuss later on). A nice discussion of the skew as the third moment of the distribution (with pretty graphs, too) can be found here.
And that's all, folks. Have a very Good Friday and a lovely Easter, and to avoid eating too many Peeps, try blasting them for a change.
Posted by kswygert at March 25, 2005 04:10 PM