Hypothesis Testing

Hypothesis testing is a powerful feature in Reveal that allows researchers to evaluate predefined or emerging hypotheses during research synthesis. This feature works similarly to the Observation Grid, enabling structured analysis of research assumptions against participant responses.

1. Adding a Hypothesis

Click Add Hypothesis to input a new assumption.
Example: There were family conflicts related to safety measures.
The hypothesis is added as a row, and Reveal automatically begins assessing it across participant responses.

2. Assessing Hypotheses

Reveal evaluates evidence from participant interviews and categorizes each hypothesis as:

✅ Pass – Evidence supports the hypothesis.
❓ Inconclusive – No clear supporting or contradicting evidence.
❌ Fail – Evidence contradicts the hypothesis.

Each participant is listed in columns, and their individual responses contribute to the overall assessment.

3. Editing Hypothesis Results

Users can manually update a hypothesis assessment if they disagree with the AI-generated result.
Simply click Edit and change the status (e.g., marking a hypothesis as "Fail" instead of "Pass").

Why Use Hypothesis Testing?

Helps validate or challenge research assumptions.
Enables quick synthesis of participant responses.
Provides structured evidence-based conclusions.

By integrating hypothesis testing into research synthesis, Reveal empowers researchers to efficiently validate insights and refine their understanding of key themes.