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The most popular theory claims  that the probability for this event is the following: P(HHHHHH)= (½*½*½*½*½*½)= 1/64 or 1.56% probability of this event occurring. This central theory, one that I taught my AP Statistics students many moons ago, asserts that as you do more trials of an independent event (like a coin toss), your results should begin to norm and approach a limit– that of the expected value of the event. Ri represents the actual value of our desired event (in this case, flipping Heads on a coin) As you can see here, the first five flips in this simulation result in H (Heads), with the last flip landing on tails. The clear conclusions we can build from these mathematical understandings is that in the case of the Iowa Caucuses last night, our sample size was too small to rely on the expected value of .5 for the six coin tosses, and that all in all, the probability of this event across a large number of samples in this event would happen about 55% of the time.