The fundamental issue of how to calculate the false positive probability of widely used Bloom Filters (BF), from which the conventional wisdom is to derive the optimal value of$k$, remains elusive. Since Bloom gave the false positive formula in 1970, in 2008, Boseet al. pointed out that Bloomˆs formula is flawed; and in 2010, Christensenet al. pointed out that Bose's formula is also flawed and gave another formula. Although Christensen's formula is perfectly accurate, it is time-consuming and impossible to calculate the optimal value of$k$. Based on the following observation: for a BF with$m$bits and$n$elements, if and only if its entropy is the largest, its false positive probability is the smallest, we propose the first approach to calculating the optimal$k$without any false positive formula. Furthermore, we propose a new and more accurate upper bound for the false positive probability. When the size of a Bloom Filter becomes infinitely large, our upper bound turns equal to the lower bound, which becomes Bloomˆs formula and deepens our understanding towards it. Besides, we derive the bounds of correct rate of Counting Bloom Filters (CBFs) by applying our proposed formulas about BFs to them.