A/B Test Hypothesis Generator

**Context**: 
A/B testing is a crucial methodology in data-driven decision-making, allowing businesses to compare two versions of a product, service, or feature to determine which one performs better. Generating hypotheses for A/B tests is a critical step in this process, as it guides the experimentation and analysis phases. A well-crafted hypothesis not only ensures that the test is meaningful but also helps in interpreting the results effectively.

**Detailed Instructions**: 
Design an A/B test hypothesis generator that can produce a set of plausible hypotheses based on a given input. The input should include the type of product or service, the specific feature or aspect to be tested, the target audience, and any relevant metrics or key performance indicators (KPIs) that the test aims to improve. The generator should consider various factors such as user behavior, market trends, and potential biases to create diverse and relevant hypotheses.

The hypotheses generated should be in the form of testable predictions that compare the original version (control group) with the modified version (treatment group). Each hypothesis should clearly state what is being tested, the expected outcome, and the criteria for success. The generator should also provide a rationale or justification for each hypothesis, explaining why it is worth testing based on current knowledge or assumptions about user behavior and market conditions.

**Output Format**: 
The output of the A/B test hypothesis generator should be a list of hypotheses, each formatted as follows:
1. **Hypothesis Statement**: A concise statement of what is being tested and the expected outcome.
2. **Variables**: Identification of the independent variable (the feature or aspect being modified) and the dependent variable (the metric or KPI being measured).
3. **Expected Outcome**: A clear description of the anticipated result, including any quantitative predictions.
4. **Success Criteria**: The metrics or thresholds that will be used to determine if the hypothesis is supported or rejected.
5. **Rationale**: A brief explanation of why this hypothesis is being proposed, including any relevant data, user insights, or theoretical foundations.

**Examples**: 
- **Hypothesis Statement**: Increasing the font size of the call-to-action (CTA) button on a landing page will lead to a higher conversion rate.
- **Variables**: Independent variable - Font size of the CTA button; Dependent variable - Conversion rate.
- **Expected Outcome**: An increase of at least 10% in the conversion rate for the treatment group compared to the control group.
- **Success Criteria**: The treatment group shows a statistically significant increase in conversion rate (p-value < 0.05) with at least a 10% difference from the control group.
- **Rationale**: Larger CTAs are more noticeable and easier to click, especially on mobile devices, which should encourage more users to complete the desired action.