It seems that lots of people (including me) like to do exploratory data analysis in Excel. Some limitations, such as the number of rows allowed in a spreadsheet, are a pain but in most cases don't make it impossible to use Excel to play around with data.

A paper by McCullough and Heiser, however, practically screams that you will get your results all wrong -- and probably burn in hell as well -- if you try to use Excel.

Is this paper correct or is it biased? The authors do sound like they hate Microsoft.

Use the right tool for the right job and exploit the strengths of the tools you are familiar with.

In Excel's case there are some salient issues:


Please don't use a spreadsheet to manage data, even if your data will fit into one. You're just asking for trouble, terrible trouble.  There is virtually no protection against typographical errors, wholesale mixing up of data, truncating data values, etc., etc.
Many of the statistical functions indeed are broken.  The t distribution is one of them.
The default graphics are awful.
It is missing some fundamental statistical graphics, especially boxplots and histograms.
The random number generator is a joke (but despite that is still effective for educational purposes).
Avoid the high-level functions and most of the add-ins; they're c**p.  But this is just a general principle of safe computing: if you're not sure what a function is doing, don't use it.  Stick to the low-level ones (which include arithmetic functions, ranking, exp, ln, trig functions, and--within limits--the normal distribution functions).  Never use an add-in that produces a graphic: it's going to be terrible.  (NB: it's dead easy to create your own probability plots from scratch.  They'll be correct and highly customizable.)


In its favor, though, are the following:


Its basic numerical calculations are as accurate as double precision floats can be.  They include some useful ones, such as log gamma.
It's quite easy to wrap a control around input boxes in a spreadsheet, making it possible to create dynamic simulations easily.
If you need to share a calculation with non-statistical people, most will have some comfort with a spreadsheet and none at all with statistical software, no matter how cheap it may be.
It's easy to write effective numerical macros, including porting old Fortran code, which is quite close to VBA.  Moreover, the execution of VBA is reasonably fast.  (For example, I have code that accurately computes non-central t distributions from scratch and three different implementations of Fast Fourier Transforms.)
It supports some effective simulation and Monte-Carlo add-ons like Crystal Ball and @Risk.  (They use their own RNGs, by the way--I checked.)
The immediacy of interacting directly with (a small set of) data is unparalleled: it's better than any stats package, Mathematica, etc.  When used as a giant calculator with loads of storage, a spreadsheet really comes into its own.
Good EDA, using robust and resistant methods, is not easy, but after you have done it once, you can set it up again quickly.  With Excel you can effectively reproduce all the calculations (although only some of the plots) in Tukey's EDA book, including median polish of n-way tables (although it's a bit cumbersome).


In direct answer to the original question, there is a bias in that paper: it focuses on the material that Excel is weakest at and that a competent statistician is least likely to use.  That's not a criticism of the paper, though, because warnings like this need to be broadcast.
