An example of a null hypothesis would be 'There is no relation between voter preference and the sex of the mayoral candidate.' The alternative hypothesis would be, ' There is a relation between voter preference and the sex of the mayoral candidate.
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Be able to reject the null hypothesis and accept the research hypothesis
A null hypothesis is written in notation by using a a statement that is the opposite of what is intended to be found, for example the research will derive answers or needed statements that is different from what is intended.
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...
Yes; the null hypothesis, H0, always includes an equality. The alternative hypothesis, H1, is >, <, or does not equal.
The difference between the null hypothesis and the alternative hypothesis are on the sense of the tests. In statistical inference, the null hypothesis should be in a positive sense such in a sense, you are testing a hypothesis you are probably sure of. In other words, the null hypothesis must be the hypothesis you are almost sure of. Just an important note, that when you are doing a tests, you are testing if a certain event probably occurs at certain level of significance. The alternative hypothesis is the opposite one.