From what I understood in my recent statistics course, the p-value is the probability of receiving a specific result considering the null hypothesis is true. It

From what I understood in my recent statistics course, the p-value is the probability of receiving a specific result considering the null hypothesis is true. It's usually compared by using some distribution model (binomial, etc.) over the actual study results. Short version; the p-value is only sufficient to disprove a hypothesis, not to prove it. If your p-value is 1% than that means you make experiments until the probability that you are wrong is less than 1%. (for example, you throw coins 100 times, 90 times is head, the probability of that result is below 0.04 so the coin is not fair) That still doesn't mean you are right. Just that at the moment you have not been prove wrong or the data is not sufficient to prove you wrong.