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.
The null hypothesis of the independent samples t-test is verbalized by either accepting or rejecting it due to the value of the t-test. If the value is less than 0.05 it is accepted and greater than 0.05 is rejecting it.
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.
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.
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)
Rejecting a true null hypothesis.
Increasing alpha from .01 to .05 will increase the probability of rejecting the null hypothesis when it is true.
Yes, usually.
sample size
An alpha error is another name in statistics for a type I error, rejecting the null hypothesis when the null hypothesis is true.
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.
No. Rejecting the Null Hypothesis means that there is a high degree of probability that it is not correct. This degree of probability is the critical level that you choose for the test statistic. However, there is still a small probability that the null hypothesis was correct.
Rejecting a true null hypothesis.
This will reduce the type 1 error. Since type 1 error is rejecting the null hypothesis when it is true, decreasing alpha (or p value) decreases the risk of rejecting the null hypothesis.
I believe you have to design a null hypothesis that is very precise in order to avoid false positives ( rejecting the null hypothesis when it is actually true). Tricky question though!
Probability of rejecting a true null hypothesis; that is, the alpha value or risk you are willing to take probabilistically speaking.
The process is called hypothesis testing. If you are inquiring on how to prove or disprove a claim, search for the article on wikipedia.