Must everyone be blinded?

No; different roles can have different information access.

Does blinding replace randomization?

No; they address different sources of distortion.

Does double-blind identify the exact roles?

Not reliably; specify them directly.

AI Glossary / Statistics and probability / Advanced

Blinded experiment

Definition

A blinded experiment withholds specified information about experimental conditions from people whose knowledge could influence behavior, measurement or analysis. Blinding is also called masking. It can apply to participants, people delivering an intervention, outcome assessors or analysts. A clear description identifies who was blinded, what they did not know, and how that separation was maintained.

What it means

The purpose is to reduce particular routes by which expectations can affect an experiment. A reviewer who knows which system produced an answer might judge identical content differently because of a brand preference. Removing that identity can reduce the opportunity for such influence.
Blinding does not make every part of a study unbiased. It cannot repair an unsuitable comparison group, missing observations or an outcome definition chosen after viewing results.

How it works

Identify the information that could influence each role, then arrange the workflow so that those roles do not receive it prematurely. Possible mechanisms include neutral condition codes, standardized presentation and separation between people preparing materials and people scoring them.
Random assignment determines which condition an experimental unit receives. Allocation concealment protects the assignment process from advance knowledge of the next allocation. Blinding concerns knowledge after assignment. These mechanisms can support one another but are not interchangeable.
Some interventions cannot be hidden from participants. Outcome assessors or analysts may still be blinded where feasible. Rather than relying on labels such as single-blind or double-blind, document the actual roles and information boundaries. Record accidental disclosures and when the condition identities were revealed.

Three practical examples

AI response evaluation: reviewers compare answers with model names removed and presentation order controlled, using a predefined scoring rubric.
Product comparison: participants assess coded versions of a product while separate staff maintain the code-to-version mapping.
Analysis review: analysts finalize a specified calculation using neutral group labels before the mapping to experimental conditions is released.

Think of it this way

Imagine judging two entries in a competition before learning who created them. Hiding the names does not guarantee excellent judging, but it removes one source of expectation.

Worked example

Consider an original fictional evaluation of two text-generation systems, A and B, on eight prompts. A coordinator prepares paired outputs, removes system names and assigns neutral display labels independently of system identity. Reviewers use a fixed ten-point rubric.
Suppose the recorded scores for A, after decoding, are 7, 8, 6, 9, 7, 8, 5 and 8. Scores for B are 6, 7, 7, 8, 7, 6, 6 and 7. A's total is 58 and B's is 54. Their means are 7.25 and 6.75, so the observed average difference is 0.5 points in A's favor.
The prompt-level differences A minus B are 1, 1, -1, 1, 0, 2, -1 and 1. A scores higher on five prompts, B on two, and one is tied.
To illustrate a possible expectation effect, imagine a deliberately stipulated alternative scoring rule: an unblinded reviewer adds one point to every output labeled A and changes no other score. No A score exceeds ten after this change. A's total would become 66 and its mean 8.25, while B would remain at 6.75. The apparent mean difference would rise from 0.5 to 1.5 points.
This alternative is a mathematical illustration of label-driven scoring, not a measured effect of blinding. A genuine comparison of blinded and unblinded evaluation would require its own design, because different reviewers, repeated exposure and other changes could affect scores.
Now consider presentation order. If A always appeared first, hiding names would not remove a first-position preference. With eight pairs, the coordinator could use a randomized balanced order placing A first on four prompts and second on four. Identity masking and order control address different problems.
Suppose a file name accidentally reveals the producing system for one prompt before scoring. The team records that disclosure. For a sensitivity illustration, imagine it was the sixth prompt, whose paired difference is two. The total paired difference across all prompts is four; excluding the sixth leaves a sum of two across seven prompts, or approximately 0.2857 points. An exclusion should not be chosen merely because it makes a preferred result stronger; the handling rule and both relevant results should be reported.
Finally, the eight prompts are the paired units in this example. If several reviewers score the same prompt, those ratings are not automatically independent new prompts. Blinding does not determine the correct unit of analysis or solve dependence. The example supports arithmetic and design explanations, not a significance claim or a conclusion that one system is generally superior.

Illustrative case study

Fictional company Willow Evaluation prepares a comparison of two summarization systems. One team generates and codes the outputs; another scores them using a documented rubric. The coordinator checks names, file metadata and obvious formatting cues before review.
The report identifies the blinded roles and acknowledges that writing style may still suggest a system's identity. Analysts record any disclosures and preserve the planned analysis. They distinguish the observed comparison from claims about how much bias blinding removed.

Common misconceptions and mistakes

Randomized does not mean blinded, and blinded does not mean randomized. A neutral label also does not guarantee successful masking if the content or surrounding metadata reveals the condition.
Blinding is not the same as anonymizing participants for privacy. Another mistake is assuming that an objective-looking outcome makes all knowledge effects impossible: knowledge can still affect how an experiment is conducted or which observations are retained. Lack of feasible participant blinding should be described specifically rather than treating every role as necessarily unblinded.

Business use

Blinding can strengthen comparisons where expectations about brands, teams or systems might affect judgments. Useful documentation identifies the condition mapping, access responsibilities, scoring rules and timing of disclosure. The result should be interpreted alongside assignment methods, sample selection, dependence and missing data, rather than treating masking as a complete guarantee of validity.

Check your understanding

What was the observed mean score difference?

Answer: Half a point.

Why did neutral labels not solve order effects?

Answer: A condition could still consistently appear first.

Why record the disclosed identity?

Answer: It changed the information available during assessment.

Related terms

Allocation concealment
Sensitivity analysis
Randomization
Observer bias
Outcome assessment
Experimental design
Paired comparison

Learn next

Design a coded evaluation workflow and identify every point where condition identity or presentation order could reach a reviewer.

Sources

SPIRIT–CONSORT, SPIRIT 2025 explanation, Item 24a on blinding status: https://www.consort-spirit.org/spiriteande24a/item-34 . CONSORT 2025 statement, items 18 and 20: https://pmc.ncbi.nlm.nih.gov/articles/PMC11996237/ . The scoring data, disclosure calculation and company scenario are original illustrations.

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