If a hypothesis is rejected because it was disproved, then one can rephrase the hypothesis to exclude the disproven data. For example: Hypothesis: White appears black. Data: When viewed by the naked eye in daylight, white appears differently than black. When viewed by the naked eye in darkness, white appears black. Reword hypothesis: In darkness, white appears black.
When we've proven that the hypothesis is false !
alternitive hypothesis
You should reject the null hypothesis.
Be able to reject the null hypothesis and accept the research hypothesis
It means that the experiment is consistent with the hypothesis. It adds to the credibility of the hypothesis.
The null hypothesis will not reject - it is a hypothesis and is not capable of rejecting anything. The critical region consists of the values of the test statistic where YOU will reject the null hypothesis in favour of the expressed alternative hypothesis.
When we've proven that the hypothesis is false !
If we reject the null hypothesis, we conclude that the alternative hypothesis which is the alpha risk is true. The null hypothesis is used in statistics.
alternitive hypothesis
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.
the hypothesis has not been proven wrong.
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.
True because the point of the hypothesis test is to figure out the probability of the null hypothesis being true or false. If it is tested and it is true, then you do not reject but you reject it, when it is false.
In statistics, we have to test the hypothesis i.e., null hypothesis and alternative hypothesis. In testing, most of the time we reject the null hypothesis, then using this power function result, then tell what is the probability to reject null 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