Standard Deviation is perhaps the least understood statistics commonly shown in data tables. The following article is intended to explain their meaning and provide additional insight on how they are used in data analysis. Both statistics are typically shown with the mean of a variable, and in a sense, they both speak about the mean.They are often referred to as the “standard deviation of the mean” However, they are not interchangeable and represent very different concepts.
Standard Deviation (often abbreviated as “Std Dev” or “SD”) provides an indication of how far the individual
responses to a question vary or “deviate” from the mean. SD tells the researcher how spread out the responses
are — are they concentrated around the mean, or scattered far & wide? Did all of your respondents rate your
product in the middle of your scale, or did some love it and some hate it? Let’s say you’ve asked respondents to rate your product on a series of attributes on a 5-point scale. The mean for a group of ten respondents (labeled ‘A’ through ‘J’ below) for “good value for the money” was 3.2 with a SD of 0.4 and the mean for “product reliability” was 3.4 with a SD of 2.1. At first glance (looking at the means only) it would seem that reliability was rated higher than value. But the higher SD for reliability could indicate (as shown in the distribution below) that responses were very polarized, where most respondents had no reliability issues (rated the attribute a “5”), but a smaller, but important segment of respondents, had a reliability problem and rated the attribute “1”. Looking at the mean alone tells only part of the story, yet all too often, this is what researchers focus on. The distribution of responses is important to consider and the SD provides a valuable descriptive measure of this.
Technical disclaimer: thinking of the Standard Deviation as an “average deviation” is an excellent way of
conceptionally understanding its meaning. However, it is not actually calculated as an average (if it were, we
would call it the “average deviation”). Instead, it is “standardized,” a somewhat complex method of computing the
value using the sum of the squares. For practical purposes, the computation is not important. Most tabulation
programs, spreadsheets or other data management tools will calculate the SD for you. More important is to
understand what the statistics convey.