Editor’s note: Richard McCullough is president of Macro Consulting Inc., Palo Alto, Calif.

In a career that is quickly - too quickly - approaching 30 years in length, I have stumbled upon a series of general principles that have been proven, usually by my not employing them, to successfully guide the earnest analyst as he or she tentatively picks his or her way through that dark and tangled forest that we often refer to as a commercial data set - all this in his or her quest for the Holy Grail of data analysis: Truth.

Thus, this article humbly serves to summarize these laws and their accompanying theorems and corollaries in much the same way as Maxwell summarized the laws of electricity and magnetism over 140 years ago. Yes, I know. I’m a bit behind.

If you still have trouble understanding the difference between accepting the null hypothesis and failing to reject it, you will find the first law extremely useful. Forget all that conceptual nonsense and apply the first law with vigor. You’ll be fine.

McCullough’s First Law of Statistical Analysis: If the statistics say an effect is real, it probably is. If the statistics say an effect is not real, it might be anyway.

This is true because none of you bother to look at beta errors (don’t worry, I don’t either). I mean, who’s got the sample size, anyway? If you do worry about such things as beta errors and power curves (and you know who you are), either you are an academic (and should have stopped reading this article long ago) or you are in desperate need of an appropriate 12-step program. When in doubt, see your nearest HR representative.

Douglas MacLachlan, a distinguished professor at the University of Washington, was kind enough to let me repeat a law he often shares with his graduate students, which captures the spirit and intent of my first law very well:

MacLachlan’s Law: Torture any data set long enough, and it will confe...