Preregistration for AI Product Comparisons: A Practical Template for Small Newsrooms

The purpose of preregistration in product research

A product comparison often involves more decisions than its final chart suggests. Researchers choose tasks, prompts, evaluation criteria, sample sizes, exclusions, and ways of summarizing performance. If those choices are made after the results become visible, it can be difficult for readers to distinguish an original research plan from decisions influenced by the findings.

Preregistration creates a dated record of the intended approach. It does not require a newsroom to predict every result or prohibit later changes. Instead, it establishes which questions and procedures came first. When the final article differs from the original plan, the differences can be explained without presenting the revised analysis as though it had always been intended.

For a small newsroom, the document can be brief. A shared file with a version history may be sufficient for internal documentation, although a public timestamped repository provides a stronger independent record. The appropriate arrangement depends on the sensitivity of the research materials and the publication's disclosure policy.

Define the comparison before choosing the headline

Begin with a question that can be answered by the proposed design. For example: How often do two AI writing assistants produce a complete answer to a fixed set of 100 editorial tasks under specified conditions? This is more useful than asking which assistant is better. It identifies a comparison, an outcome, and a bounded set of observations.

Next, specify the population or conditions to which the findings might apply. Are the products being compared on publicly accessible versions, organizational accounts, or a particular configuration? Are the tasks intended to represent short newsroom assignments, research summaries, or another defined activity? The intended scope should appear in the research plan and the eventual headline.

A product comparison also needs a clear account of what is held constant and what is allowed to vary. Different default settings, context windows, account permissions, or tool access can influence outcomes. If a difference is part of the product experience, document it. If it is a potential confounder, explain how the design addresses it.

• State the research question in one sentence.
• Identify the products, configurations, and comparison conditions.
• Define the target tasks and the intended scope of the findings.
• Specify the primary outcome and how it will be measured.
• Record the analysis plan and rules for exclusions.

A compact preregistration template

The following structure is designed for a newsroom with limited research capacity. It records the decisions most likely to affect interpretation without requiring a lengthy academic protocol. Each field should be completed before examining the comparison outcomes. If a detail genuinely cannot be specified in advance, state why and describe how it will be handled.

For example, a newsroom might plan to compare two hypothetical writing assistants on 100 tasks, with three independent evaluations per task. It could define completion as satisfying every mandatory element in a task-specific rubric. The plan should also say how disagreements between evaluators will be handled and whether the final outcome is calculated per response or per task.

• Research question: What specific difference is being investigated?
• Products and conditions: Which configurations, access levels, and settings are included?
• Sample: How many tasks or observations are planned, and how are they selected?
• Primary outcome: What observable measure will answer the main question?
• Secondary outcomes: Which additional measures will be reported as exploratory or supplementary?
• Analysis and exclusions: Which calculations, missing-data rules, and exclusion criteria will be applied?

Usage data have limits as evidence

Product comparisons increasingly rely on usage data such as session counts, messages sent, time spent, return visits, or feature activation. These measures can describe observable behavior, but they do not automatically reveal why someone used a product or whether the experience was beneficial. A higher session count might reflect sustained interest, repeated attempts to obtain an answer, or a workflow that requires more interactions.

Consider a hypothetical comparison in which one chat application records an average of 40 messages per active user each week and another records 25. Those figures describe message volume under the companies' definitions. They do not establish that users prefer the first application, find it more useful, or achieve better outcomes. The meaning depends on how active users are defined, what counts as a message, and which users are included.

Correlation versus causation is particularly important when analyzing chat app behavior. If users who send more messages also report higher satisfaction, that association does not establish that additional messages caused satisfaction. Existing preferences, usage intensity, task complexity, or other characteristics may influence both measures. A causal interpretation requires a design capable of addressing those alternative explanations.

The preregistration should therefore distinguish behavioral measures from claims about attitudes, outcomes, or causes. It should identify whether the data come from direct observation, self-report, or vendor analytics. Data access rules, retention periods, account-level exclusions, and aggregation procedures also affect what can be interpreted and reproduced.

Analysis decisions that should be recorded in advance

A research plan should identify one primary outcome. Without that decision, a comparison can produce many possible charts, each emphasizing a different difference. Additional outcomes are useful, but readers should know which one was intended to answer the central question. A newsroom can also state in advance whether it will report uncertainty intervals and how it will handle multiple comparisons.

Exclusions deserve particular attention. A failed response, technical interruption, incomplete task, or ambiguous evaluation can affect the final result. The plan should define which cases qualify for exclusion and whether they will remain visible in supplementary reporting. If the rule changes, the final article should state the change and explain its effect.

Analysis should also distinguish independent observations from repeated ones. If the same task is submitted several times, those responses may not provide the same information as observations from several independent tasks. The statistical method should reflect the actual structure of the data rather than treating every recorded response as interchangeable.

Deviations and publication are part of the record

Research rarely unfolds exactly as planned. A product may change its interface, an account may lose access to a feature, or a planned task may become impossible to complete. The right response is to document the event, record the date and consequences, and distinguish the original plan from the final procedure. Preregistration is a record of intent, not a requirement to preserve a flawed design.

Exploratory analysis should remain available when it reveals an interesting pattern. The important distinction is labeling. A question generated after examining the data should not be described as a hypothesis that was specified beforehand. A transparent article can report both planned findings and later observations while explaining their different evidential status.

A reproducibility checklist should accompany the publication where practical. It can include the research plan, task definitions, evaluation rubric, data dictionary, analysis code, product configuration, collection period, and a record of deviations. Sensitive user data should not be published simply to make a study reproducible; privacy and access restrictions need to be considered separately.

• Preserve the original preregistration and its timestamp.
• Document changes to tasks, settings, collection, and analysis.
• Label post hoc findings as exploratory.
• Publish the methods and analysis materials that can responsibly be shared.
• Explain limitations that prevent independent reproduction.

Questions readers ask