# Independent samples and ANOVA

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1. What is an independent (non related) sample? What is a related (dependent) sample? When should researchers use different hypothesis tests for independent and related samples? Is one type of sample preferable over the other? Provide an example of an independent sample and or a related sample that might be appropriate for use in healthcare management. Explain rationale.

2. Why is the F distribution important? How do you determine if a significant difference exists among the groups in ANOVA? How do you determine differences between the groups in ANOVA? ANOVA are easy to construct. Provide an example using either a personal or professional experience.

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##### Solution Summary

Explains the difference between independent and dependent samples, when dependent samples should be used, and whether there are advantages of one type over the other. Gives an example of a dependent (related) sample.

Explains the use of the F distribution in ANOVA, how group differences are determined in ANOVA, and gives an example of a study that would use ANOVA.

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1. I related sample is one where the people in each sample are related in some way to one another, so that you may see some correlation between their scores on your measure. For example, if you have brothers in one sample and their sisters in another sample, you may see a correlation in IQ or personality type between the two samples. Sometimes related samples are created by using repeated measures, where the same people are tested twice. For example, maybe you're evaluating a weight-loss program and you measure each of your participants 3 times over the course of three months. Since you are measuring the same people repeatedly, you have measurements at the 3 times related to one another. With independent samples, you don't expect knowing something about one person's score to tell you anything about people's scores in the other sample. There is no relationship between the two samples, so you can't predict one sample's scores on a measure by knowing scores on that measure for the other sample.

Related samples are often desirable because they get rid of a lot of noise in the data. If you're measuring the ...

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