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docs: link the rapidata-vs-prolific quality benchmark #765

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2 changes: 2 additions & 0 deletions docs/understanding_the_results.md
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Expand Up @@ -187,6 +187,8 @@ Qualification tasks are examples with known correct answers that labelers must p
- **Quality Control**: It helps in identifying and filtering for the most reliable responses.
- **Insight into Labeler Performance**: Provides transparency into who is contributing to your data and how reliably.

We measured how well this works in practice by asking Rapidata's crowd and Prolific's panel the same 300 questions with known answers: [Rapidata vs. Prolific - Cost and Quality on the Same 300 Tasks](https://www.rapidata.ai/blog/rapidata-vs-prolific).

## Utilizing the Results

- **Clear Winners**: Use `winner` / `winnerIndex` (the raw-majority winner) or `weightedWinner` / `weightedWinnerIndex` (the reliability-weighted winner) to identify which option was preferred — and be explicit about which notion of "won" you mean, since the two can differ on close votes. Both are `null` when there is no clear winner. The older `winner_index` is a deprecated alias of `winnerIndex`; prefer `winnerIndex`.
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