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demonstrates the probability of error when asserting that there is a difference before and after expansion.Every research is subjected to some degree of error, given that we are not investigating an entire population, but only a fraction, a sample. For this reason, when we compare two samples undergoing different treatment procedures with a view to identi -fying the most efficient therapy, we will always have the chance of having reached a wrong conclusion. Therefore, the lower the p-value is, the smaller the chance of error and, as a result, the more certain we are to assure that treatment “A” is more efficient than “B”.But, how can we control a false-positive error? Initially, we have to decide on the significance level (α) we expect to establish. In Dentistry, we usually set a significance level not greater than 5% (α = 5%). Nevertheless, should we increase the number of comparisons of a given study, we increase the chanc -es of yielding outcomes that are due just to chance and, as a consequence, finding a false-positive result. The lottery is a good example. The chances of win-ning are little, less than 5%. However, the more we bet, the higher our chances of winning.In statistical tests, there is a dramatic increase in false-positive rates, in which the number of com-parisons is directly proportional to the number of false-positive results, as shown in Table 1.Thus, when we make several comparisons using a simple statistical test, we significantly increase the chances of yielding a false-positive result. Table 1 demonstrates that the chances of yielding a false-positive result are of 40% for a study involving 10 comparisons. In these cases, some adjustments are necessary to keep the significance level set at 5%. One of the procedures employed to correct false-positive rates is the Bonferroni correction. It con-sists of dividing the significance level by the number of comparisons made in a given study.

Controlling false positive rates in research and its clinical implications