The Minimally Meaningful Effect Size: A Vital Component of Pre-Registrations
Bibliographic record
Abstract
Psychology is currently experiencing a replication crisis, wherein many attempts to replicate past studies have failed. Pre-registration is a valuable resource to enhance the replicability of research and maintain scientific rigour. However, the incorporation of information regarding effect size magnitude, namely the minimally meaningful effect size (MMES), has yet to be present within pre-registrations. Incorporating an MMES in pre-registrations encourages researchers to make a priori considerations regarding the minimum effect size threshold that must be met for a particular effect to be practically significant. This threshold depends on the research context, which includes study specific factors that interact with the effect size magnitude to determine the meaningfulness of an effect. Pre-registering the MMES will discourage researchers from haphazardly interpreting the magnitude of an effect (HIMEing; e.g., modifying post hoc what is considered meaningful), a behaviour that we deem a questionable research practice. Using a previously published study, we provide an example of how to incorporate the MMES into pre-registrations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.516 | 0.867 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".