Significance Tests / Hypothesis Testing  Jerry Dallal
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Significance Tests / Hypothesis Testing
How do you know which hypothesis to put in H_{0} and which one to put in H_{a}? Typically, the null hypothesis says that nothing new is happening; the previous result is the same now as it was before, or the groups have the same average (their difference is equal to zero). In general, you assume that people’s claims are true until proven otherwise. So the question becomes: Can you prove otherwise? In other words, can you show sufficient evidence to reject H_{0}?
Analyze cases to either reject or not reject a null hypothesis.
Download the Unit 4 Assignment 1 Answer Template from the Resources area and use the template to complete the following sections:
• Section 1: z Scores in SPSS.
• Section 2: Case Studies of Type I and Type II Errors.
• Section 3: Case Studies of Null Hypothesis Testing.
Format your answers in narrative style, integrating supporting statistical output (table and graphs) into the narrative in the appropriate places (not all at the end of the document).
Statistics How To: Elementary Statistics for the rest of us!
However, if you do not reject the null hypothesis, you cannot accept the alternative hypothesis as true.
You have simply failed to find statistical justification to reject the alternative hypothesis.
Type I and Type II Errors
If you commit a Type I error, this means that you have incorrectly rejected a true null hypothesis.
i) State the null alternative hypotheses, explaining your choice.
ii)Calculate the Pvalue
iii)Represent your results on a graph that marks clearly the rejection and non rejection regions. What do you concludes.
iv) Construct a 90% confidence interval around the March sample mean. Comment on the, relative to your conclusion in part (iii)
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When you about a , you can use your test statistic to decide whether to reject the null hypothesis, H_{0}. You make this decision by coming up with a number, called a value.
Keep in mind that you will probably never know whether the null hypothesis is “true” or not,
as we can only determine that our data fail to reject it.
If you commit a Type II error, this means that you have not rejected a false null hypothesis when you should
have rejected it.
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Several conventional tests have some degree ofrobustness. For example, Welch's (1938) ttest used by SPSS andSatterthwaite's (1946) ttest used by SAS could compensate unequalvariances between two groups. In SAS when you run a ttest, SAS canalso test the hypothesis of equal variances. When this hypothesis isrejected, you can choose the ttest adjusted for unequal variances.