you do not need to reject a null hypothesis. If you don not that means "we retain the null hypothesis."
we retain the null hypothesis when the p-value is large but you have to compare the p-values with alpha levels of .01,.1, and .05 (most common alpha levels). If p-value is above alpha levels then we fail to reject the null hypothesis.
retaining the null hypothesis means that we have evidence that something is going to occur (depending on the question)
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.
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.
We have two types of hypothesis i.e., Null Hypothesis and Alternative Hypothesis. we take null hypothesis as the same statement given in the problem. Alternative hypothesis is the statement that is complementary to null hypothesis. When our calculated value is less than the tabulated value, we accept null hypothesis otherwise we reject null hypothesis.
The z-score is a statistical test of significance to help you determine if you should accept or reject the null-hypothesis; whereas the p-value gives you the probability that you were wrong to reject the null-hypothesis. (The null-hypothesis proposes that NO statistical significance exists in a set of observations).
The observed value is unlikely to have occured purely bt chance under the null hypothesis and, as a consequence, you ought to reject the null in favour of the alternative hypothesis.
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.
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 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.
At a probability of 0.5 you cannot reject the null hypothesis!
You should reject the null hypothesis.
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
Be able to reject the null hypothesis and accept the research hypothesis
You reject the null hypothesis if the probability of the observed outcome, calculated under the null hypothesis, is smaller than some preset level. Commonly used levels are 10%, 5%, 1% or 0.1%.
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.
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.