Suppose that a learning algorithm is trying to find a consistent hypothesis when the classifications of examples are actually random. There are n Boolean attributes, and examples are drawn uniformly from the set of 2^n possible examples. Calculate the number of examples required before the probability of finding a contradiction in the data reaches 0.5.

Note:In this scenario, the goal is to find how many examples are required before the probability of finding a contradiction in the data reaches 0.5. As such, using an equation you must arrive at a numerical answer for this scenario.