Hypothesis Generator
Generate testable research hypotheses from observations
Overview
Generate testable null and alternative hypotheses for academic research, UX experiments, and data science studies. Takes your observations and variables, outputs properly formatted H0 and H1 statements with testable predictions and confounding variable analysis. Works for A/B tests, user research studies, scientific experiments, and statistical analysis projects.
If you’re running experiments without formal hypotheses, your results lack statistical rigor and reproducibility. This template structures your observations into falsifiable statements that peer reviewers and stakeholders actually accept. Handles everything from clinical trials to product analytics.
Use Cases
- Turn 6 months of user behavior data into testable hypotheses for product experiments
- Generate null and alternative hypotheses for dissertation research proposals in under 3 minutes
- Create statistically sound hypothesis pairs for A/B tests on SaaS conversion funnels
- Format observational data from user interviews into research questions for UX studies
- Build hypothesis frameworks for machine learning model validation and testing
- Structure preliminary findings into testable predictions for grant proposals and funding applications
Benefits
- 3-minute setup - input observations, get publication-ready hypotheses
- Identify confounding variables you’d miss manually, saving weeks of flawed research
- Statistical rigor - generates properly paired null and alternative hypotheses that satisfy peer review requirements
- Covers edge cases - suggests control variables and potential biases specific to your study design
- Works across disciplines - handles social science, life science, data science, and product research formats
Template
Generate testable research hypotheses based on the following:
Research Topic: {{topic}}
Observations:
{{observations}}
Variables:
- Independent: {{independentVariable}}
- Dependent: {{dependentVariable}}
- Control: {{controlVariables}}
Research Context: {{context}}
Please generate:
1. Null hypothesis (H0)
2. Alternative hypothesis (H1)
3. Testable predictions
4. Expected outcomes
5. Potential confounding variables
Properties
- topic: Single-line Text
- observations: Multi-line Text
- independentVariable: Single-line Text
- dependentVariable: Single-line Text
- controlVariables (optional): Single-line Text (default:
None) - context: Multi-line Text
Example Output
Input:
- Topic: Mobile app push notifications and user engagement
- Observations: Users who receive personalized push notifications show 23% higher daily active usage compared to users receiving generic notifications. However, users who receive more than 3 notifications per day show a 15% increase in app uninstalls.
- Independent Variable: Notification personalization level (generic vs. personalized)
- Dependent Variable: Daily active user rate and app retention
- Control Variables: Notification frequency (held at 2 per day), user demographics, time of day sent
- Context: Mobile productivity app with 50K monthly active users. Current notification system sends generic reminders. Team wants to test if AI-powered personalized notifications improve engagement without increasing uninstalls.
Generated Output:
1. Null Hypothesis (H0)
Personalized push notifications have no significant effect on daily active user rate or app retention compared to generic notifications when notification frequency is controlled at 2 per day.
2. Alternative Hypothesis (H1)
Personalized push notifications significantly increase daily active user rate and app retention compared to generic notifications when notification frequency is controlled at 2 per day.
3. Testable Predictions
Primary Predictions:
- Users receiving personalized notifications will show 15-25% higher daily active usage vs. control group
- App uninstall rate will remain stable or decrease compared to generic notifications at 2/day frequency
- 7-day retention will increase by 10-18% for personalized notification group
Secondary Predictions:
- Notification tap-through rate will be 30-40% higher for personalized vs. generic
- Time spent in-app per session will increase by 8-12% for personalized group
- Users will opt-out of notifications at similar or lower rates compared to generic notifications
4. Expected Outcomes
Success Scenario:
- Daily active users increase by 18-23% (matching observation data)
- Uninstall rate remains below 5% increase (well under the 15% threshold at 3+ notifications/day)
- Retention improvement of 12-15% over 30-day period
Metrics to Track:
- DAU/MAU ratio
- 1-day, 7-day, 30-day retention rates
- Notification opt-out rate
- App uninstall rate
- Notification engagement rate (opens, taps)
- Session duration and frequency
5. Potential Confounding Variables
User-Level:
- Prior app usage patterns (power users vs. casual users)
- User lifecycle stage (new vs. established users)
- Device type (iOS vs. Android notification behavior differences)
- Geographic location and time zone effects
- Language and cultural preferences
Technical:
- Notification delivery reliability across platforms
- App version differences during test period
- OS-level notification permission changes
- Network connectivity affecting delivery
Temporal:
- Seasonality (weekday vs. weekend behavior)
- External events (holidays, news events)
- Concurrent product changes or campaigns
- Day-of-week and time-of-day variations
Behavioral:
- Novelty effect (initial boost that decays over time)
- User learning curve with new notification style
- Cross-contamination between test groups (users discussing the app)
Recommendation: Run A/B test with 10K users per group (personalized vs. generic) for 4-6 weeks to account for novelty effects and establish statistical significance while monitoring for confounding variables.
Common Mistakes When Writing Research Hypotheses
Confusing correlation with causation Your observations show correlation, but your hypothesis must specify causal direction. “Users who engage more receive more emails” isn’t testable: does engagement cause emails or vice versa? Specify which variable you’re manipulating.
Vague dependent variables “Improve user experience” can’t be measured. Use specific metrics like task completion time, error rate, or satisfaction scores. If you can’t put a number on it, you can’t test it.
Ignoring statistical power A hypothesis predicting a 2% conversion lift needs thousands of users to detect. Small sample sizes mean you need large effect sizes. Factor in your available sample when writing predictions.
Skipping the null hypothesis Many researchers write only H1. Statistical tests evaluate evidence against H0, not for H1. Both are required for proper experimental design.
One-tailed when you need two-tailed If your intervention could make things worse, use a two-tailed test. Predicting “personalization increases engagement” ignores the possibility it might decrease it, like when notifications become creepy.
Forgetting confounds in observational data Survey data and analytics aren’t experiments. Users who find your feature might already be power users. List every alternative explanation before claiming your variable caused the effect.
Frequently Used With
Hypothesis generation typically requires supporting research tasks:
- Research Summary - synthesize findings after testing your hypotheses
- Methodology Design - build the experimental design to test your hypotheses
- Data Collection Plan - structure how you’ll gather data to validate predictions
- Literature Review - review existing research before forming new hypotheses
- Interview Guide - collect qualitative observations to inform hypothesis formation
- Data Analysis Report - analyze results against your predictions