In addition to the very informative answer that Peter provided above, you should also take into serious consideration all of the descriptive statistics derived from your sample. Especially when it comes to optimum investment option selecting, skewness of your data plays crucial role in deciding which one would be the most promising in terms of profitability.
For instance, suppose that you have this sample: $1000,$1500,$1300,$1400,$1350,$1550,$1250,$1100,$10000,$1150,$1280. This sample implies CV=38,15% and mean profit=$2080
Then we have another sample:
$2000,$2160,$1960,$2200,-$4000,$8160,-$10000,$14160,-$15000,$19160,$2080 wich implies CV=141% and mean profit=2080
At first glance,whereas both of the samples have the same mean, we would opt for the first option as the optimum investment since it implies the smallest CV, but if you examine more thoroughly both of the data, you will find out that in the first sample we have an extreme value ($10000) wich significantly affects the distribution of the data (positive skewness),fact that renders them unreliable.
As far as the second sample is concerned, we can distinguish from the graph (and from the descriptive statistics of course) that the data is normally distributed around the mean, fact that makes them more consistent to rely on and take decissions based on them, even though it has larger volatility.
Ιn conclusion, the real challenge of a researcher is whether he/she should exclude or not the extreme values of the sample and how he/she justify such action.