In practice, an error probability of might not be acceptable, however, the choice of in the definition is arbitrary. It can be any constant between 0 and (exclusive) and the set BPP will be unchanged. It does not even have to be constant: the same class of problems is defined by allowing error as high as − "n"−"c" on the one hand, or requiring error as small as 2−"nc" on the other hand, where "c" is any positive constant, and "n" is the length of input. The idea is that there is a probability of error, but if the algorithm is run many times, the chance that the majority of the runs are wrong drops off exponentially as a consequence of the Chernoff bound. This makes it possible to create a highly accurate algorithm by merely running the algorithm several times and taking a "majority vote" of the answers. For example, if one defined the class with the restriction that the algorithm can be wrong with probability at most , this would result in the same class of problems.