There is a common argument among psychologists that null results are uninformative. Part of this is the logic of NHST – failure to reject the null is not the same as confirmation of the null. Which is an internally valid statement, but ignores the fact that studies with good power also have good precision to estimate effects.
However there is a second line of argument which is more procedural. The argument is that a null result can happen when an experimenter makes a mistake in either the design or execution of a study. I have heard this many times; this argument is central to an essay that Jason Mitchell recently posted arguing that null replications have no evidentiary value. (The essay said other things too, and has generated some discussion online; see e.g., Chris Said’s response.)
The problem with this argument is that experimental errors (in both design and execution) can produce all kinds of results, not just the null. Confounds, artifacts, failures of blinding procedures, demand characteristics, outliers and other violations of statistical assumptions, etc. can all produce non-null effects in data. When it comes to experimenter error, there is nothing special about the null.
Moreover, we commit a serious oversight when we use substantive results as the sole evidence of procedures. Say that the scientific hypothesis is that X causes Y. So we design an experiment with an operationalization of X, O_X, and an operationalization of Y, O_Y. A “positive” result tells us O_X -> O_Y. But unless we can say something about the relationships between O_X and X and between O_Y and Y, the result tells us nothing about X and Y.
We have a well established framework for doing that with measurements: construct validation. We expect that measures can and should be validated independent of results to document that Y -> O_Y (convergent validity) and P, Q, R, etc. !-> O_Y (discriminant validity). We have papers showing that measurement procedures are generally valid (in fact these are some of our most-cited papers!). And we typically expect papers that apply previously-established measurement procedures to show that the procedure worked in a particular sample, e.g. by reporting reliability, factor structure, correlations with other measures, etc.
Although we do not seem to publish as many validation papers on experimental manipulations as on measurements, the logic of validation applies just as well. We can obtain evidence that O_X -> X, for example by showing that experimental O_X affects already-established measurements O_X2, O_X3, etc. And in a sufficiently powered design we can show that O_X does not meaningfully influence other variables that are known to affect Y or O_Y. Just as with measurements, we can accumulate this evidence in systematic investigations to show that procedures are generally effective, and then when labs use the procedures to test substantive hypotheses they can run manipulation checks to show that they are executing a procedure correctly.
Programmatic validation is not always necessary — some experimental procedures are so face-valid that we are willing to accept that O_X -> X without a validation study. Likewise for some measurements. That is totally fine, as long as there is no double standard. But in situations where we would be willing to question whether a null result is informative, we should also be willing to question whether a non-null is. We need to evaluate methods in ways that do not depend on whether those methods give us results we like — for experimental manipulations and measurements alike.
The Hardest Science 