Random Sampling And Random Assignment

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Let’s work through an example and see how it combats confounding variables.

Take the biomechanics study where we wanted to see if the jumping exercise (treatment group) produced greater bone density than the group that didn’t jump (control group).

You should also measure and include everything that you think is important.

Of course, understanding and measuring everything of importance may not be possible due to time and cost constraints.

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To learn more or modify/prevent the use of cookies, see our Cookie Policy and Privacy Policy.My past several posts have detailed confounding variables, a problem you might encounter in research or quality improvement projects.To recap, confounding variables are correlated predictors.Indeed, all of the relevant variables may not be known or even measurable. There is a simple solution to this complex problem.You can wave the white flag and admit that you don’t know everything, or at least that you can’t measure everything that affects your response. Randomness plays several important roles in the design of experiments.For example, random selection might be used to draw 100 students to participate in a study.Each of these 100 participants would then be randomly assigned to either the control group or the experimental group.Scenario 2: We use random assignment so the treatment and control groups start out with roughly equal levels of physical activity.Activity still affects bone density but it is equally spread across the groups.For both scenarios, the data and statistical results could be identical.However, the results for the second scenario are more valid thanks to the methodology.

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