Increasing alpha from .01 to .05 will increase the probability of rejecting the null hypothesis when it is true.
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.
Probability of rejecting a true null hypothesis; that is, the alpha value or risk you are willing to take probabilistically speaking.
Statistical tests compare the observed (or more extreme) values against what would be expected if the null hypothesis were true. If the probability of the observation is high you would retain the null hypothesis, if the probability is low you reject the null hypothesis. The thresholds for high or low probability are usually set arbitrarily at 5%, 1% etc. Strictly speaking, when rejecting the null hypothesis, you do not accept the alternative hypothesis because it is possible that neither are true and it is the model itself that is wrong.
Be able to reject the null hypothesis and accept the research hypothesis
a small standard error and a large alpha level
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.
Rejecting null hypothesis means that rumour of a fact is been experimented in d lab and the reseacher later discovered that hypothesis pseudo i.e fack fact but not a theory
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.