Probability predictions are often surprising. In the case of the coin-tossing experiment, Dr. Hill wrote in the current issue of the magazine American Scientist, a ''quite involved calculation'' revealed a surprising probability. It showed, he said, that the overwhelming odds are that at some point in a series of 200 tosses, either heads or tails will come up six or more times in a row. Most fakers don't know this and avoid guessing long runs of heads or tails, which they mistakenly believe to be improbable. At just a glance, Dr. Hill can see whether or not a student's 200 coin-toss results contain a run of six heads or tails; if they don't, the student is branded a fake.

Even more astonishing are the effects of Benford's Law on number sequences. Intuitively, most people assume that in a string of numbers sampled randomly from some body of data, the first non-zero digit could be any number from 1 through 9. All nine numbers would be regarded as equally probable.

But, as Dr. Benford discovered, in a huge assortment of number sequences -- random samples from a day's stock quotations, a tournament's tennis scores, the numbers on the front page of The New York Times, the populations of towns, electricity bills in the Solomon Islands, the molecular weights of compounds, the half-lives of radioactive atoms and much more -- this is not so.

Given a string of at least four numbers sampled from one or more of these sets of data, the chance that the first digit will be 1 is not one in nine, as many people would imagine; according to Benford's Law, it is 30.1 percent, or nearly one in three. The chance that the first number in the string will be 2 is only 17.6 percent, and the probabilities that successive numbers will be the first digit decline smoothly up to 9, which has only a 4.6 percent chance.

A strange feature of these probabilities is that they are ''scale invariant'' and ''base invariant.'' For example, it doesn't matter whether the numbers are based on the dollar prices of stocks or their prices in yen or marks, nor does it matter if the numbers are in terms of stocks per dollar; provided there are enough numbers in the sample, the first digit of the sequence is more likely to be 1 than any other.

The larger and more varied the sampling of numbers from different data sets, mathematicians have found, the more closely the distribution of numbers approaches what Benford's Law predicted.

One of the experts putting this discovery to practical use is Dr. Mark J. Nigrini, an accounting consultant affiliated with the University of Kansas who this month joins the faculty of Southern Methodist University in Dallas.