I am having some difficulties with understanding exactly what I need to do. I know how to obtain my independent and dependent variable, however, I am having trouble bringing together being able to generate the statistical null and alternative hypotheses.
Using one of the following:
Ho: mu = (hypothesized value) vs. H1: mu not equal to (hypothesized value)
Ho: mu >= (hypothesized value) vs. H1: mu < (hypothesized value)
Ho: mu <= (hypothesized value) vs. H1: mu > (hypothesized value)
and using realistic numbers for values of degrees of freedom, sample size, and t statistic, report hypothetical results in a few sentences using correct APA format.© BrainMass Inc. brainmass.com March 21, 2019, 2:58 pm ad1c9bdddf
I hope this helps!!! See the attached Word file for the same information.
First, how to obtain the null and alternative hypotheses...
The null hypothesis is the hypothesis that you are testing. It is the default situation, and is the condition that you accept as true unless you get evidence to the contrary.
The alternative hypothesis is the hypotheses that you think (or hope) will be true based on your prior knowledge of the situation.
One of the most confusing things about using statistical tests is knowing what it means to reject or accept a hypothesis. Before you conduct a statistical test, you come up with a null and an alternative hypothesis. Usually the null hypothesis is something like "the populations means of the two groups are equal," and the alternative hypothesis is "the means are different."
Then, when you do the statistical test, you get a p-value, which is basically the probability that the null hypothesis is true. So, if you get a really small p-value (usually the cut-off points are 0.05 or 0.01), you can reject the null hypothesis. Notice that this is not the same as definitively stating that the alternative hypothesis is true. For example, if you were testing if the mean of a ...
The solution fully explains null and alternative hypotheses, provides complete examples of one-sample z-tests and t-tests, and explains the difference between p-value and effect size.