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You calculate standard deviation the same way as always. You find the mean, and then you sum the squares of the deviations of the samples from the means, divide by N-1, and then take the square root. This has nothing to do with whether you have a normal distribution or not. This is how you calculate sample standard deviation, where the mean is determined along with the standard deviation, and the N-1 factor represents the loss of a degree of freedom in doing so. If you knew the mean a priori, you could calculate standard deviation of the sample, and only use N, instead of N-1.
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When computing Standard deviation should you eliminate the extreme values?
Generally not without further reason. Extreme values are often called outliers. Eliminating unusually high values will lower the standard deviation. You may want to calculate standard deviations with and without the extreme values to identify their impact on calculations. See related link for additional discussion.
What is 2 Standard Deviations above an average of .1407?
The answer depends on the value of the standard deviation. Without that information, the question cannot be answered.
What is meant by dispersion without deviation?
when a ray of light enters two specifically arranged prisms and disperese i.e. splits into characteristic colours without suffering any deviation inside the prisms(the magnitude of deviation for both the prisms is same and in opposite direction, so net deviation is zero); its called dispersion without deviation...
Is it possible for the standard deviation to be less than the sample mean?
Absolutely. In fact I would commonly expect it to be. If, for example, the sample mean for the length of a bolt was 5.5 cm, you would certainly hope the standard deviation was a lot less than 5.5 cm. or it would imply bolts with a negative length (not quite sure how you'd do that without breaching some alternate dimension - no pun intended.)
How can you use statistical measures to compare populations?
There are different methods for comparing the mean, variance or standard error, distribution or other characteristics of populations. Without more specific information it is not possible to answer the question.
