Differences between parametric dependent and independent samples
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What are differences between parametric dependent and independent samples? My facilitator has been known to describe a scenario with two objects and an associated measurement versus another scenario with one object and two associated measurements in his on ground classes. Which is which? Provide concrete examples of each. Then, explain the difference from nonparametric dependent and independent samples. What are implications for determining the tests used to analyze data? How is parametric data different from nonparametric data?
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This solution highlights the differences between the parametric dependent and independent samples, and between nonparametric and independent samples. The solution answers several additional and related questions including explaining the difference between parametric and nonparametric data.
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What are differences between parametric dependent and independent samples?
Parametric samples are dependent if samples are related to members of the other sample. That is, the same (or related) set of subjects or data sources are used to generate the samples. These samples are also called matched or paired. Each data point in one sample has a matching (paired) data point in the other. Parametric samples are independent if members of one sample are unrelated to members of the other sample. Independent sample are those in which different (unrelated) data sources are used.
Examples of a dependent sample design are:
Human subjects are weighed before and after a two-week exercise program. Each subject contributes one data point to each of the two datasets. Sample A (Before) and Sample B (After) are also dependent because the members of each are related. If an individual is placed into Sample A (before), the same individual must be in Sample B (after).
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Sample A (Husbands) and Sample B (Wives) are dependent because the members of each are related. If a husband goes into Sample A, ...
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