Random assignment: Difference between revisions

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To express this same idea statistically - If a randomly assigned group is compared to the [[average|mean]] it may be discovered that they differ, even though they were assigned from the same group. If a test of [[statistical significance]] is applied to randomly assigned groups to test the difference between sample [[average|mean]]s against the [[null hypothesis]] that they are equal to the same population mean (i.e., population mean of differences = 0), given the probability distribution, the null hypothesis will sometimes be "rejected," that is, deemed not plausible. That is, the groups will be sufficiently different on the variable tested to conclude statistically that they did not come from the same population, even though, procedurally, they were assigned from the same total group. For example, using random assignment may create an assignment to groups that has 20 blue-eyed people and 5 brown-eyed people in one group. This is a rare event under random assignment, but it could happen, and when it does it might add some doubt to the causal agent in the experimental hypothesis.

==Random Samplingsampling==

Random sampling is a related, but distinct process.<ref name="socialresearchmethods.net">{{cite web|url=http://www.socialresearchmethods.net/kb/random.php}}</ref> Random sampling refers tois recruiting participants in a way that they represent a larger population.<ref name="socialresearchmethods.net"/> Because most basic statistical tests require the hypothesis of an independent randomly sampled population, random assignment is the desired assignment method because it provides control for all attributes of the members of the samples—in contrast to matching on only one or more variables—and provides the mathematical basis for estimating the likelihood of group equivalence for characteristics one is interested in, both for pretreatment checks on equivalence and the evaluation of post treatment results using inferential statistics. More advanced statistical modeling can be used to adapt the inference to the sampling method.

==History==