Implications of Difference in Standard Deviation Size
This week, we are covering an important concept that is widely used in a variety of fields: the standard
deviation. The standard deviation is a measure of how close together a set of data is grouped.
In Chapter 2 of your text, Kozak (2014) stated:
“In general a “small” standard deviation means the data is close together (more consistent) and a
“large” standard deviation means the data is spread out (less consistent). Sometimes you want
consistent data and sometimes you don’t. As an example if you are making bolts, you want to lengths to
be very consistent so you want a small standard deviation. If you are administering a test to see who can
be a pilot, you want a large standard deviation so you can tell who are the good pilots and who are the
bad ones.” (p. 89).
Example Situations Involving Pilots
A large standard deviation tells us that there is a lot of variability in the scores; that is, the distribution
of scores is spread out and not clustered around the mean. As Kozak (2104) has stated, when assessing
potential pilots, we may want a large standard deviation, so that we can differentiate between
candidates; that is, we can determine who would be good pilots and those who would not be good
pilots. Since a large standard deviation tells us that there is a lot of variability in the scores, candidates,
who would be good pilots, would have scores far above the mean, while those who would not be good
pilots would have scores far below the mean. Thus, only the "top" candidates would be selected to be
A small standard deviation tells us that there is not a lot of variability in a distribution of scores; that is,
the scores are very consistent (similar) and close to the mean. Using our pilot example, a small standard
deviation is desirable, when considering aircraft landing distances. If there is not enough distance when
landing, the aircraft could undershoot the runway; that is, land short of the runway. On the other hand,
if there is too much distance, the aircraft can overshoot the runway; that is, the aircraft does not stop
before the end of the runway. Both undershooting and overshooting runways could result in injuries
and/or fatalities. Thus, it is important that pilots have consistent accurate landing distances. Specifically,
over a number of landings, the distances would, on average, be appropriate to the runway length with
little variation among the distances.
Now think about how this might apply in your chosen field and answer both of the following questions:
What is an example of when you would want consistent data and, therefore, a small standard
What is an example of when you might want a large standard deviation? That is, data that is
more spread out?