Most of the people who do these studies don't understand that either. But what I want to know is what your quote was from. Whatever is being described - I think I want some of that.

Here's the simple version: say you think that A can have an effect on B ... so you set up an experiment to test that hypothesis. Conventional research says that what you want to test is the Null Hypothesis and the P value is the point at which you ARBITRARILY decide that you would 'reject' the Null Hypothesis in favour of the Alternate Hypothesis. So one Null Hypothesis is that there is no effect, or that the effect will be more or less than a pre-determined value. You would then run the experiment, collect the data and analyse it using statistical methods. What you are looking for is this: Is this effect, if it exists, due to chance or something else? If the p-value is greater than the preset amount then you would basically fail to reject the Null Hypothesis ... if it is smaller, then you are basically saying that the 'chance of this effect occurring, if it exists, is less than simple chance' and you would reject the Null Hypothesis of no effect' in favour of the Alternate Hypothesis that says that if there is an effect, the likelihood of it occurring is less than chance. Where many 'researchers' mess up is the belief that a smaller p-value is somehow more significant when in fact it is simply less likely to occur by chance but still only indicates you can reject the Null Hypothesis. Without knowing what the research is about, what the previous research found, what the effect size might be and the sample size (which directly affects the p-value) then most of those P-values are meaningless garbage.

Its important to realize that studies are not in themselves the holy grail. They are important in their validation or invalidation of an assertion or theory. It's just another step in a very long and tedious process.