You should reject the null hypothesis.
Be able to reject the null hypothesis and accept the research hypothesis
alternitive hypothesis
It means that the experiment is consistent with the hypothesis. It adds to the credibility of the hypothesis.
When we've proven that the hypothesis is false !
It means that she or he has to accept that the existing hypothesis appears to be true.
Some people say you can either accept the null hypothesis or reject it. However, there are statisticians that insist you can either reject it or fail to reject it, but you can't accept it because then you're saying it's true. If you fail to reject it, you're only claiming that the data wasn't strong enough to convince you to choose the alternative hypothesis over the null hypothesis.
You should reject the null hypothesis.
Depending on the results of that test, either accept or reject that hypothesis.
if the hypothesis is proven to be correct or incorrect
Be able to reject the null hypothesis and accept the research hypothesis
Be able to reject the null hypothesis and accept the research hypothesis
Be able to reject the null hypothesis and accept the research hypothesis
This is used in statistic to know whether to accept or reject a null hypothesis or alternative hypothesis
alternitive hypothesis
reject
Some researchers say that a hypothesis test can have one of two outcomes: you accept the null hypothesis or you reject the null hypothesis. Many statisticians, however, take issue with the notion of "accepting the null hypothesis." Instead, they say: you reject the null hypothesis or you fail to reject the null hypothesis. Why the distinction between "acceptance" and "failure to reject?" Acceptance implies that the null hypothesis is true. Failure to reject implies that the data are not sufficiently persuasive for us to prefer the alternative hypothesis over the null hypothesis.