How to check for normal distribution using Excel for performing a t-test? I want to know how to check a data set for normality in Excel, just to verify that the requirements for using a t-test are being met.  
For the right tail, is it appropriate to just calculate a mean and standard deviation, add 1, 2 & 3 standard deviations from the mean to create a range then compare that to the normal 68/95/99.7 for the standard normal distribution after using the norm.dist function in excel to test each standard deviation value.
Or is there a better way to test for normality?
 A: You could plot a histogram using the data analysis toolpack in Excel. Graphical approaches are more likely to communicate the degree of non-normality, which is typically more relevant for assumption testing (see this discussion of normality). 
The data analysis toolpack in Excel will also give you skewness and kurtosis if you ask for descriptive statistics and choose the "summary statistics" option. You might for example consider values of skewness above plus or minus one be a form of substantive non-normality. 
That said, the assumption with t-tests is that the residuals are normally distributed and not the variable. Furthermore, they also quite robust such that even with fairly large amounts of non-normality, p-values are still fairly valid.
A: This question borders on statistics theory too - testing for normality with limited data may be questionable (although we all have done this from time to time).
As an alternative, you can look at kurtosis and skewness coefficients.  From Hahn and Shapiro: Statistical Models in Engineering some background is provided on the properties Beta1 and Beta2 (pages 42 to 49) and the Fig 6-1 of Page 197.  Additional theory behind this can be found on Wikipedia (see Pearson Distribution).
Basically you need to calculate the so-called properties Beta1 and Beta2.  A Beta1 = 0 and Beta2 = 3 suggests that the data set approaches normality.  This is a rough test but with limited data it could be argued that any test could be considered a rough one.
Beta1 is related to the moments 2 and 3, or variance and skewness, respectively.  In Excel, these are VAR and SKEW.  Where ... is your data array, the formula is:
Beta1 = SKEW(...)^2/VAR(...)^3

Beta2 is related to the moments 2 and 4, or the variance and kurtosis, respectively.  In Excel, these are VAR and KURT.  Where ... is your data array, the formula is:
Beta2 = KURT(...)/VAR(...)^2

Then you can check these against the values of 0 and 3, respectively.  This has the advantage of potentially identifying other distributions (including Pearson Distributions I, I(U), I(J), II, II(U), III, IV, V, VI, VII).  For example, many of the commonly used distributions such as Uniform, Normal, Student's t, Beta, Gamma, Exponential, and Log-Normal can be indicated from these properties:
Where:   0 <= Beta1 <= 4
         1 <= Beta2 <= 10 

Uniform:        [0,1.8]                                 [point]
Exponential:    [4,9]                                   [point] 
Normal:         [0,3]                                   [point]
Students-t:     (0,3) to [0,10]                         [line]
Lognormal:      (0,3) to [3.6,10]                       [line]
Gamma:          (0,3) to (4,9)                          [line]
Beta:           (0,3) to (4,9), (0,1.8) to (4,9)        [area]
Beta J:         (0,1.8) to (4,9), (0,1.8) to [4,6*]     [area]
Beta U:         (0,1.8) to (4,6), [0,1] to [4.5)        [area]
Impossible:     (0,1) to (4.5), (0,1) to (4,1]          [area]
Undefined:      (0,3) to (3.6,10), (0,10) to (3.6,10)   [area]

Values of Beta1, Beta2 where brackets mean:

[ ] : includes (closed)
( ) : approaches but does not include (open)
 *  : approximate 

These are illustrated in Hahn and Shapiro Fig 6-1.
Granted this is a very rough test (with some issues) but you may want to consider it as a preliminary check before going to a more rigorous method.
There are also adjustment mechanisms to the calculation of Beta1 and Beta2 where data is limited - but that is beyond this post.
A: You have the right idea.  This can be done systematically, comprehensively, and with relatively simple calculations.  A graph of the results is called a normal probability plot (or sometimes a P-P plot).  From it you can see much more detail than appears in other graphical representations, especially histograms, and with a little practice you can even learn to determine ways to re-express your data to make them closer to Normal in situations where that is warranted.
Here is an example:

Data are in column A (and named Data).  The rest is all calculation, although you can control the "hinge rank" value used to fit a reference line to the plot.
This plot is a scatterplot comparing the data to values that would be attained by numbers drawn independently from a standard Normal distribution.  When the points line up along the diagonal, they are close to Normal; horizontal departures (along the data axis) indicate departures from normality.  In this example the points are remarkably close to the reference line; the largest departure occurs at the highest value, which is about $1.5$ units to the left of the line.  Thus we see at a glance that these data are very close to Normally distributed but perhaps have a slightly "light" right tail.  This is perfectly fine for applying a t-test.
The comparison values on the vertical axis are computed in two steps.  First each data value is ranked from $1$ through $n$, the amount of data (shown in the Count field in cell F2).  These are proportionally converted to values in the range $0$ to $1$.  A good formula to use is $\left(\text{rank}-1/6\right)/\left(n+2/3\right).$  (See http://www.quantdec.com/envstats/notes/class_02/characterizing_distributions.htm for where that comes from.)  Then these are converted to standard Normal values via the NormSInv function.  These values appear in the Normal score column.  The plot at the right is an XY scatterplot of Normal Score against the data.  (In some references you will see the transpose of this plot, which perhaps is more natural, but Excel prefers to place the leftmost column on the horizontal axis and the rightmost column on the vertical axis, so I have let it do what it prefers.)

(As you can see, I simulated these data with independent random draws from a Normal distribution with mean $5$ and standard deviation $2$.  It is therefore no surprise that the probability plot looks so nice.)  There really are only two formulas to type in, which you propagate downward to match the data: they appear in cells B2:C2 and rely on the Count value computed in cell F2.  That's really all there is to it, apart from the plotting.
The rest of this sheet is not necessary but it's helpful for judging the plot: it provides a robust estimate of a reference line.  This is done by picking two points equally far in from the left and right of the plot and connecting them with a line.  In the example these points are the third lowest and third highest, as determined by the $3$ in the Hinge Rank cell, F3.  As a bonus, its slope and intercept are robust estimates of the standard deviation and mean of the data, respectively.
To plot the reference line, two extreme points are computed and added to the plot: their calculation occurs in columns I:J, labeled X and Y.

