The "null" hypothesis would typically be that "there is no effect". All that you can really do is prove it wrong, by measuring an effect when there "should", b

The "null" hypothesis would typically be that "there is no effect". All that you can really do is prove it wrong, by measuring an effect when there "should", by the hypothesis, be none. Due to what is known as the "problem of induction", it's not sufficient to accept a hypothesis because you appeared to not measure an effect in the past, as that says nothing about whether an effect will occur the next time a measurement is made. p-value is the "chance" of measuring an effect, given that no effect actually occurred.