reply: Hello everyone, Much like in a criminal trial where you must presume the defendant to be not guilty, you must presume the null hypothesis to be true as you start the hypothesis test. It is only when the evidence convinces you to change your minds that you reject the null hypothesis. This would be similar to proving that the defendant is guilty beyond a reasonable doubt. This would also mean that the evidence supports the alternate hypothesis. When you support the alternate hypothesis, you say that the evidence was significant. What does this mean? If you fail to reject the null hypothesis, does this mean that you believe it to be true? Why or why not? The key idea here is that failing to reject the null hypotheses does not mean that we believe it is true. Just like a not guilty verdict does not mean the person is innocent it means the evidence was not strong enough for a conviction to say otherwise. Statistics always start by assuming the null hypotheses is true. We reject it if the data provides convincing evidence to determine where our p-value falls below a preset significance level. If it does not, we do not have enough justification to support the alternative hypothesis but that does not prove the null is definitely true. Failing the to reject the null does not mean we believe it is true it just means we do not have concrete evidence to say otherwise in that moment