You may have detected a theme running through the last few sections: Over and over, we’ve noted that a particular procedure or a particular comparison might yield data that are open to more than one interpretation. Over and over, therefore, we’ve adjusted the procedure or added a precaution to avoid this sort of ambiguity. That way, when we get our result, we won’t be stuck in the position of saying that maybe this caused the result or maybe that caused the result. In other words, we want to set up the experiment from the start so that, if we observe an effect, there’s just one way to explain it. Only in that situation can we draw conclusions about the impact of our independent variable.
How have we achieved the goal? The various steps we’ve discussed all serve to isolate the experimental manipulation—so it’s the only thing that differentiates the two groups, or the two conditions, we are comparing. With random assignment, we ensure that the groups were identical (or close to it) at the start of the experiment. By properly designing our control procedure, we ensure that just one factor within the experiment distinguishes the groups, Then, if the two groups differ at the end of the study, we know that just one factor could have produced this difference—and that’s what allows us to make the strong claim that the factor we manipulated did, indeed, cause the difference we observed.
These various steps (random assignment, matching of procedures, and so on) are all aimed at ensuring that an experiment has internal validity—it has the properties that will allow us to conclude that the manipulation of the independent variable was truly the cause of the observed change in the dependent variable. If an experiment lacks internal validity, it will not support the cause-and-effect claims that our science needs.
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