# Preregistration, Registered Reports, and Open Science

## Purpose

Preregistration records a time-stamped plan before the relevant data are collected or analyzed. Its main value is making planned and data-dependent work distinguishable.

Preregistration does not:

- guarantee a valid design or analysis;
- prevent all researcher degrees of freedom;
- make a hypothesis true;
- forbid exploration or justified adaptation;
- replace ethics, safety, data, or regulatory review;
- require public release of restricted information.

## What to preregister

### Administrative

- title and project ID;
- accountable owner and roles;
- registration date and repository;
- study status and prior access to relevant data;
- conflicts, funding, and sponsor roles.

### Question and candidates

- observation and provenance;
- research question and claim type;
- candidate hypotheses and mechanisms;
- rivals and alternative explanations;
- causal estimands where applicable;
- boundary conditions and uncertainty.

### Predictions and controls

- prediction IDs and parent candidates;
- conditions, measurements, expected patterns, and timing;
- falsifiers and indeterminate outcomes;
- discriminating expectations for rivals;
- null hypotheses;
- positive, procedural, and negative controls.

### Design

- population/system and sampling;
- experimental and analysis units;
- allocation, randomization, concealment, and masking;
- interventions/exposures and comparators;
- inclusion, exclusion, attrition, and stopping;
- sample-size or precision rationale;
- outcomes and measurement timing;
- ethics, safety, data, and regulatory status.

### Analysis

- analysis populations;
- transformations and data exclusions;
- models, contrasts, estimators, and effect/summary measures;
- uncertainty intervals or other inferential summaries;
- missing-data and intercurrent-event handling;
- multiplicity;
- assumptions and diagnostics;
- sensitivity and robustness analyses;
- rules for interpreting support, challenge, and indeterminacy.

### Transparency

- data, code, materials, and metadata plans;
- restrictions and controlled-access process;
- software/environment versions;
- AI/tool use;
- deviation log and reporting plan.

## Timing and prior access

State what had already occurred:

- no data collected;
- data collected but target outcomes unseen;
- data available but analyst blinded;
- summary statistics viewed;
- exploratory analysis already performed;
- existing dataset reused.

When data have already informed the plan, label the work transparently and use independent data, a held-out set, or a new replication for confirmatory testing where feasible.

## Confirmatory versus exploratory

### Confirmatory

- planned before checking the target result;
- tied to specified outcomes and analyses;
- reported whether favorable, unfavorable, or null.

### Exploratory

- generated after or while viewing data;
- useful for discovery;
- labeled as data-dependent;
- treated as a source of future predictions.

Do not call exploratory work “post hoc confirmation.”

## HARKing

Kerr defined HARKing as presenting a post hoc hypothesis as if it were a priori. Prevent it by:

- preserving dated versions;
- separating planned and unplanned analyses;
- reporting all prespecified outcomes and tests;
- documenting when each candidate was generated;
- not rewriting unexpected results as predictions;
- seeking independent replication.

## Deviations

Preregistration is a plan, not a prison. For every material deviation record:

- date;
- affected section and IDs;
- original plan;
- change;
- reason;
- who made the decision;
- whether the target result was known;
- likely effect on bias or interpretation;
- whether the original analysis is still reported.

Do not silently replace the registration. Preserve the original and append amendments.

## Registered Reports

Registered Reports add journal peer review before results are known:

1. Stage 1 protocol submission;
2. review of question, methods, and analysis;
3. in-principle acceptance under the journal’s conditions;
4. study conduct;
5. Stage 2 review focused on adherence, justified deviations, and interpretation.

Check the current journal policy. In-principle acceptance is not ethics approval, funding, regulatory authorization, or assurance of a favorable result.

## Intervention trials

For randomized intervention hypotheses:

- use current registration requirements for the applicable jurisdiction, funder, and venue;
- use SPIRIT 2025 for protocol reporting;
- align objectives, estimands, outcomes, harms, intervention details, statistical methods, and data sharing;
- use CONSORT 2025 for completed-trial reporting;
- report important changes, including non-prespecified outcomes or analyses.

The preregistration scaffold in this skill is generic and is not a trial-registry submission, SPIRIT checklist, protocol, statistical analysis plan, or regulatory document.

## Reproducibility and replicability

Use the National Academies definitions:

- **reproducibility:** obtaining consistent computational results with the same data, code, methods, and analysis conditions;
- **replicability:** obtaining consistent results in a new study addressing the same question with new data.

Plan for:

- stable identifiers and version control;
- code and environment capture;
- provenance and decision logs;
- independent replication;
- exact and conceptual replication;
- boundary-condition and transport tests;
- publication of null and challenging results.

Non-replication does not automatically imply misconduct or that the original study was invalid. Differences can reveal heterogeneity, measurement limitations, context, or sampling variation.

## Open-science limits

“Open” does not override:

- participant consent and privacy;
- Indigenous or community data governance;
- contractual or intellectual-property restrictions;
- export controls;
- biosafety and dual-use review;
- endangered-species or sensitive-location protections;
- security-sensitive vulnerabilities.

Share the maximum responsibly permitted, not the maximum technically possible. Use metadata, synthetic examples, controlled access, or redacted protocols when full release is unsafe.

## Scaffold generation

Generate a local draft only after the hypothesis record validates:

```bash
python3 scripts/generate_preregistration_scaffold.py \
  local-hypothesis-record.json \
  -o local-preregistration.md
```

The result:

- is marked as an unregistered draft;
- includes every candidate without ranking;
- carries unresolved placeholders;
- requires human review and repository-specific completion;
- does not submit, register, upload, or transmit anything.