how to determine skewness from histogram with outliers? I have the following histogram created in Minitab. I am wondering whether this histogram is actually positively skewed, negatively skewed, or symmetric.
By observing the graph itself, it seems that it is negatively skewed. However, mean (58.08) > median (57.7). Does this mean that it is positively skewed, or fairly symmetric?
Any helpful answer will be highly appreciated :D

 A: 
I am wondering whether this histogram is actually positively skewed, negatively skewed, or symmetric.

Skewness:
Skewness isn't a well-defined thing; it's a relatively vague notion, one that there have been numerous attempts to give specific definitions of. The different definitions are not necessarily consistent with each other. For example moment skewness might suggest the opposite direction to  second Pearson skewness, (a measure based on mean minus median).
Additionally, zero skewness (by whatever single number measure) doesn't imply symmetry.
Assessing skewness from a histogram 
This is also tricky. 


*

*it's often unclear which direction, if any, the skewness might be in 

*a small change in the bin width or even just the bin origin can completely change the impression of skewness. If you only use a few bins (as you have in your diagram), this risk is greater. So sometimes even when it looks clear, the impression may be a false one.
[Unfortunately, stats packages tend to base their bin widths off certain optimal formulae, but the thing they optimize is not especially suited for vidually judging the shape and identifying fine detail. Our eyes can smooth roughness, and default histograms tend to be much too smooth for such purposes. I typically increase the number of bins by a factor of 2 or often much more.]
See this answer assessing approximate distribution of data based on a histogram which discusses some of the issues, but I'll give some highlights here -
The difference between these two histograms boils down to a simple change of bin origin:

And the difference between these two is a change of binwidth (and origin):

That is, the same data can look quite different, depending on how you set up your plot. For more details and advice, see the above-linked answer.
Looking at your plot
Keeping those caveats in mind, you might either describe that plot of yours as appearing mildly right skew but with an extreme low outlier, or possibly as appearing left skew because of the low outlier, or simply as appearing asymmetric. 
I think most people might say something more like the first, but it's just a guess.  The last one is a pretty safe assessment, but it's not especially helpful.
