# Trust & Data Quality (https://terac.com/docs/researchers/trust-and-quality)

How Terac works to ensure your data comes from real, verified humans, and what you can do to protect quality.

The value of your research depends on who is behind each response. Terac is built around a simple premise: the people completing your study should be real, verified humans who are a genuine fit for what you are studying. This section explains the safeguards that support that, and how you can use them.

## The Layers [#the-layers]

Data quality on Terac is not a single feature. It is a set of layers that each remove a different risk.

Verified people → Fit screening → Quality review → Secure handling

| Layer                     | What it protects against                              | Where to read more                                                                                       |
| ------------------------- | ----------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
| **Verified participants** | Fake, duplicate, or misrepresented identities         | [Verified Participants](https://terac.com/docs/researchers/trust-and-quality/verified-participants)                       |
| **Fit screening**         | The right humans, but the wrong people for your study | [Screening](https://terac.com/docs/researchers/opportunities/screening), [Filters](https://terac.com/docs/researchers/recruitment/filters) |
| **Quality review**        | Low-effort, incomplete, or dishonest submissions      | [Quality Controls](https://terac.com/docs/researchers/trust-and-quality/quality-controls)                                 |
| **Secure handling**       | Mishandled or over-collected personal data            | [Data Handling](https://terac.com/docs/researchers/trust-and-quality/data-handling)                                       |

## Your Part [#your-part]

The platform handles identity and provides the review tooling, but study-level quality is a shared responsibility. The practices that make the biggest difference:

* **Screen for fit, not just identity.** Use [filters](https://terac.com/docs/researchers/recruitment/filters) for hard requirements and [screening questions](https://terac.com/docs/researchers/opportunities/screening) for judged criteria.
* **Design fair checks.** Attention and comprehension checks help, but only when they are fair. See [Avoiding Bias](https://terac.com/docs/researchers/research-methods/avoiding-bias).
* **Review deliberately.** Choose a [review method](https://terac.com/docs/researchers/results/reviewing-work) that matches the stakes of your study.
* **Pay fairly and instruct clearly.** Both reduce low-effort responding.

## Start Here [#start-here]

- [Verified Participants](https://terac.com/docs/researchers/trust-and-quality/verified-participants): How identity and attestations work
- [Quality Controls](https://terac.com/docs/researchers/trust-and-quality/quality-controls): Review methods, AI moderation, and quality signals
- [Data Handling](https://terac.com/docs/researchers/trust-and-quality/data-handling): What data you receive, and your responsibilities