Best statistics to show outliers beyond 2 SD I try to perform scheduling for an organization based on client flows.  We are very seasonal.  Our senior management in Washington, DC make us base our schedule on the mean number of clients we have annually.  However, in summer months the number of clients we see on a daily basis are two SD above the mean.   In the winter, on a daily basis, we see very few clients (on many days two or three SD below the mean).  I would like to convince management we need to develop two schedules one for the winter (where we are currently over staffed because staffing is based on the annual mean number of clients) and one for the summer (where we are currently severly understaffed because staffing, again, is based on the annual mean number of clients we serve).  I have used bar charts showing the numbers above and below 2 SD, but I have to believe there is a more robust stastical argument and graph I can use to make my point. Any suggestions would be greatly appreciated.
 A: Based on your description, it sounds as though the idea of viewing the number of clients only at the annual level will lead to very poor predictions at the seasonal level. So all you need to do is convince management that there is a very substantial difference across the different seasons (rather than spending lots of time examining outliers). 
I think the best way to do this is to look at boxplots of the different seasons. You should expect that Summer and Winter look very different. 
A: Outliers, by definition are unusual, rare events.
A predictable, seasonal, trend thus is not meaningfully represented by "outliers". If done properly, you should have only about 1 outlier day per year, not many.
Furthermore, unless you have days with negative amount of customers, your data cannot be normal distributed!
For these reasons, I believe you are using the wrong statistical tools.
I suggest using a plot to show the seasonality, and measure how well it is fit by different models: flat staffing, vs. seasonal staffing. Try to make a good prediction on the month level for which months how much staff is needed.
