As indicated in my previous post:
It has no other significance.

The figures I posted were not those of an actual normal distribution curve - they were just my manual approximation of what one might look like for data spanning the number range you posted. The actual curve might be flatter or steeper than my figures suggest. In any event, for so few results, it would be unreasonable to expect a close approximation of the true normal distribution curve. You might need a few hundred results before you would have a really close approximation of the true normal distribution curve - it all depends on how variable the data are. For example, to attain 90% confidence that your sample data are within 5% of a normal distribution, you'd need a minimum sample size of 271. 95% confidence would require a minimum sample size of 385.

If you need to analyse the data statistically, you'll need some understanding of the theory. You should perhaps get a copy of Freund's Modern Elementary Statisics. See also:
http://en.wikipedia.org/wiki/Normal_distribution
http://en.wikipedia.org/wiki/Sample_size

__________________
Cheers,
Paul Edstein
[Fmr MS MVP - Word]

Many thanks Paul for the answer.

I thought there might be a simple tool that can check a set of data whether they are normally distributed or not.

I think that I have sufficient background regarding this topic. My issue is that to use the normal distribution function (attached), it is required first to check whether the data is normally distributed. I attempt to find a tool that does this sort of work but sounds that this a bit confusing.

In other words, having normally distributed data is a condition to apply the normal distribution function for further analysis! But how can we first check our raw data (whether it is normally distributed or not?!)

Best

Jamal

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