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The approach to proving a hypothesis is to evaluate the null hypothesis (a statement that is supported by measurements that only occur randomly and show no statistical significance beyond random occurrances. If there is no difference between expected and measured values than this supports the null hypothesis. The only way to prove a hypothesis is to DISprove the null. And to disprove the null the differences between the expected and measured need to be significant. Here is the brain twister for me: So, we have a hypothesis (idea) that we have to support may be true. To do that we instead turn our attention to the NULL hypothesis. If we can prove that it is NOT true, it gives evidence that the hypothesis is true by default. So, back to the p-value. If the probability is so low that our findings are by chance we can "reject the Null hypothesis". By rejecting it, the hypothesis wins! "If null isn't true that hypothesis must be true".