Hypothesis Testing for Means and Proportions

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1. Think about a real world: Diagnosis Congestive heart failure (CHF), non compliance with daily weight or diet/situation. Address using an independent or related samples t test.
2. Identify the independent (grouping) and dependant (response) variables important to study 3. Explain whether an independent sample or related sample t test is appropriate and why 4. Generate a hypothesis, including null and alternative hypothesis 5. Describe what information the effect size will tell you and what information the effect size will tell you and what information the p value or critical value approach will not 6. Using realistic numbers for the degrees of freedom, sample size and t statistic, report hypothetical results in 2-3 sentences

Solution: (1) Let’s consider the following research situation: The incidence of Congestive heart failure (CHF) is going to be studied based on two different diet groups: one group receives a special diet (a diet designed for preventing CHF), and a control group (which doesn’t receive any diet). We are interested in assessing whether there is a difference in the incidence of CHF for these two groups. In order to perform the analysis, a two-independent t-test will be used.

(2) In this case, the independent (grouping) variable is DIET, and the dependent (response) variable is CHF incidence rate.

(3) This analysis corresponds to an independent-samples design, because the treatments (diet/no diet) are applied to different subjects.

(4) We are interested in the following research question:

Is there a difference in the incidence of CHF for the diet and no-diet group?

The following hypotheses are used:

[pic]

where [pic]represents the mean CHF incidence rate.

(5) The information given by the p-value is about SIGNIFICANCE, which means the probability of getting sampling results as extreme or more extreme than the ones obtained, under the assumption that the null hypothesis true. The problem with this information is that a...
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