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.