<P> Imagine an experiment in which the participants are not randomly assigned; perhaps the first 10 people to arrive are assigned to the Experimental Group, and the last 10 people to arrive are assigned to the Control group . At the end of the experiment, the experimenter finds differences between the Experimental group and the Control group, and claims these differences are a result of the experimental procedure . However, they also may be due to some other preexisting attribute of the participants, e.g. people who arrive early versus people who arrive late . </P> <P> Imagine the experimenter instead uses a coin flip to randomly assign participants . If the coin lands heads - up, the participant is assigned to the Experimental Group . If the coin lands tails - up, the participant is assigned to the Control Group . At the end of the experiment, the experimenter finds differences between the Experimental group and the Control group . Because each participant had an equal chance of being placed in any group, it is unlikely the differences could be attributable to some other preexisting attribute of the participant, e.g. those who arrived on time versus late . </P> <P> Random assignment does not guarantee that the groups are matched or equivalent . The groups may still differ on some preexisting attribute due to chance . The use of random assignment cannot eliminate this possibility, but it greatly reduces it . </P> <P> To express this same idea statistically - If a randomly assigned group is compared to the 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 means 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 . </P>

Random assignment help the researcher make sure that