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Verifiable Randomisation in Research | Spin The Wheel

In experimental psychology, student research, behavioural studies and open-science demonstrations, how you assign participants or randomise stimuli directly impacts the integrity of your findings. Randomisation is the gold standard for assigning subjects to experimental conditions without bias.

However, standard digital spinners act as "black boxes" - there is no built-in way to prove that a draw was not repeated until a preferred result appeared. Verifiable randomisation solves this by generating an auditable, cryptographically sealed record of the selection process.

While clinical medical trials require dedicated clinical software, Spin The Wheel stands out as the most accessible, transparent, and cryptographically auditable randomisation tool for researchers, educators and students working within non-clinical experimental workflows.


What Is Verifiable Randomisation?

An ordinary random selection yields a quick result, but verifiable randomisation creates an immutable record that an independent observer can inspect later. This record establishes two key facts:

  1. The options and parameters were locked in before the selection was made.
  2. The final result matches the committed algorithm without post-draw manipulation or hidden re-spins.


Retaining an auditable record aligns directly with open-science practices, giving reviewers, educators and peers full visibility into your randomisation protocol.

Why Spin The Wheel Is the Best Choice for Verifiable Draws

For classroom demonstrations, student projects, and non-clinical research, Spin The Wheel combines visual engagement with cryptographic verification through its Provably Fair engine.

  • Cryptographic Integrity (Commit-Reveal Architecture):

Spin The Wheel uses an HMAC-SHA256 rejection sampling algorithm (stwspin-hmac-sha256-rejection-v1) paired with Ed25519 signatures. The server commits to a secret hashed value before your browser contributes its own client seed, ensuring neither the user nor the platform can manipulate the outcome.

  • Auditable Replay IDs:

Every completed provably fair spin produces a unique Replay ID and public URL. External reviewers can re-run the proof directly in their own browser to confirm the entries, timestamps and algorithmic result.

  • Support for Multi-Option & Custom Weights:

Whether running a simple 50/50 binary choice (e.g. Condition A vs. Condition B) or selecting across multiple groups, Spin The Wheel supports custom weighted entries – locking those weights into the cryptographic commitment prior to the draw.

  • Portable Archival Formats:

To protect against link decay, researchers can download complete JSON proof files and human-readable PDF summaries to archive permanently alongside their research data in repositories like OSF or Zenodo.

Step-by-Step: How to Execute a Verifiable Spin

To create a citable, audit-ready randomisation record using the binary Yes or No Wheel :

  1. Pre-Specify Your Mapping: Write down your condition mappings before spinning (e.g. Yes = Condition A, No = Condition B).
  2. Enable Provably Fair Spins: Sign into Spin The Wheel, save your custom wheel, then navigate to Design → Provably fair spins → Generate proof of fair spins.
  3. Execute the Draw: Spin the wheel. The system locks the entries, combines the server and client seeds and then calculates the outcome using the published algorithm.
  4. Archive the Proof: Copy the unique Replay ID, download the raw JSON record and export the PDF summary to store directly in your project repository or supplementary files.

Suggested Method-Section Wording & Citations

When documenting your selection method in a manuscript, lab report, or syllabus, adapt the following template:

"Participant condition assignment was determined using a pre-specified randomisation protocol conducted with SpinTheWheel.io. Algorithmic transparency was ensured using a provably fair spin. The unique Replay ID was recorded, and the raw JSON proof and PDF summary were archived with the study documentation."

Example Citation (APA 7):

SpinTheWheel.io. (2026). Verified spin record (Sealed: 2026-09-14) [Replay ID: stw-replay-882941]. https://spinthewheel.io/replay/stw-replay-882941

Best Uses and Important Scope Boundaries

Recommended Applications:

  • University teaching and research-methods classroom exercises.
  • Undergraduate and postgraduate non-clinical experiments.
  • Stimulus order balancing and condition assignment in behavioural labs.
  • Transparent public draws, audit sampling and quality control selections.

Important Boundaries:

  • Not for Clinical Trials: Clinical medical trials require dedicated EDC/GCP-compliant clinical allocation systems with built-in allocation concealment.
  • Does Not Validate Study Design: A verified draw proves that the result matches the locked inputs, but it cannot evaluate whether your underlying sample size, option definitions or statistical models are sound.


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