Preregistration increases the informativeness of your data for theories

Theories predict observations. Observations are either consistent or inconsistent with the theory that that implied the observations. Observations that are consistent with the theory are said to corroborate the theory. Observations that are inconsistent with the theory should cast doubt on the theory, or cast doubt on one of the premises of the theory.

There are a few things that affect the extent to which data inform theory. Below I argue that preregistration can strengthen the informativeness of data for a theory in a few ways.

First, data only informs theory via a chain of auxiliary assumptions. All else being equal, data that inform a theory through fewer auxiliary assumptions are more informative to that theory than data that make contact with theory through more auxiliary assumptions.

For example, data are informative to a particular theory so long as readers assume the predictor is valid, the outcome is valid, the conditions relating the predictor to the outcome have been realized, the sample was not selected based on the obtained results, the stated hypotheses were not modified to match the obtained results, etc. Sometimes these auxiliary assumptions are not accepted (by some individuals) and the theory is treated (by some individuals) as uninformed by the data.

Preregistration can essentially eliminate some assumptions that are required to interpret the data. Readers do not need to accept the assumption of which hypotheses were indeed a priori. Readers do not need to accept the assumption of how the sample was determined. Etc. There is a date-stamped document declaring all of these features. The only assumption necessary is that the preregistration is legit. Thus, all else being equal, a preregistered study has fewer links in the chain of auxiliary assumptions linking the observed data to the theory that is being tested. Thus, all else being equal, data from a preregistered study are more informative to a theory than data from a non-preregistered study.

Second, the informativeness of the data is related to the degree to which the data can be consistent or inconsistent with the theory. If a theory really sticks its neck out there, the data can more strongly corroborate or disconfirm the theory. If a theory does not stick its neck out there, the data are less informative to a theory, regardless of the specific pattern of data.

Preregistration clearly specifies which outcomes are predicted and which are not prior to the data being analyzed. Predictions that were made prior to the data being analyzed are riskier than predictions that were made after the data have been analyzed. Why? Because with preregistration there is no ambiguity in whether the predictions were actually made independently of the results of those predictions. Preregistered predictions stick their neck out. Very specific preregistered predictions really stick their neck out.

I am not saying that you must preregister your studies. I am saying that choosing not to preregister your studies is also choosing not to maximize the informativeness of your data for your theories.