Hypothesis grouping is a powerful organizational system that helps you manage experiments by their underlying testing theory. Instead of treating each experiment as an isolated test, hypothesis grouping allows you to organize multiple related experiments around a central idea or theory about what drives conversions.
In the context of PressPlay, a hypothesis is a testable theory about what might improve your app store conversion rate. A hypothesis groups together multiple experiments that all test the same underlying assumption about user behavior.
"Users respond better to lifestyle imagery than UI screenshots" - This hypothesis might include multiple screenshot experiments showing people using the app in real-world situations
"Emphasizing privacy and security features increases downloads" - This could group icon experiments, short description variants, and screenshots all highlighting security features
"Simpler, more minimalist icons perform better than detailed ones" - This would organize various icon design experiments testing different levels of visual complexity
"Action-oriented language drives higher engagement" - This groups text experiments using verbs and imperative statements
Organizing experiments into hypothesis groups provides several benefits:
Hypothesis grouping transforms random testing into strategic research. You're not just trying different variants—you're systematically investigating theories about what resonates with your audience.
When multiple experiments within a hypothesis show consistent results (all winners or all losers), you gain confident insights about user preferences. If results are mixed, you learn that the hypothesis needs refinement.
Over time, validated hypotheses become a knowledge base for your app store optimization strategy. You develop a deeper understanding of your audience beyond individual test results.
Grouping related experiments allows you to review and approve multiple tests together, streamlining your workflow. If you validate a hypothesis, you can confidently approve all experiments within it.
Hypotheses help you prioritize which experiments to approve and deploy first. Test your highest-conviction hypotheses before exploring more speculative ideas.
You can create hypothesis groups in several ways within PressPlay:
When reviewing experiments, click the "Add to Hypothesis" button on any experiment card. If no suitable hypothesis exists, select "Create New Hypothesis" and provide:
Hypothesis Name - A clear, descriptive title (e.g., "Lifestyle imagery outperforms UI screenshots")
Hypothesis Description - Detailed explanation of the theory you're testing and why you believe it will improve performance
Expected Impact - What metrics you expect this hypothesis to influence (CVR, impression-to-install rate, etc.)
Priority Level - High, medium, or low based on your conviction and potential impact
Navigate to the Hypothesis section from the main menu to access the dedicated hypothesis management interface. This view allows you to create hypotheses proactively before experiments are generated, helping guide the AI toward your strategic priorities.
When configuring AI experiment generation, you can specify hypotheses you want the AI to explore. The AI will then generate experiments aligned with those testing theories.
Once you've created hypothesis groups, you can organize experiments into them:
From any experiment card in the review queue:
Click "Add to Hypothesis"
Select from existing hypotheses or create a new one
Confirm the assignment
The experiment is now grouped under that hypothesis for organizational and analytical purposes.
For multiple related experiments:
Enable multi-select mode in the review queue
Select all experiments related to a single hypothesis
Click "Assign to Hypothesis" in the bulk actions toolbar
Choose the hypothesis or create a new one
Confirm to assign all selected experiments at once
PressPlay's AI can automatically suggest which hypothesis an experiment belongs to based on:
The experiment's design characteristics
The generation prompt used to create it
Similarity to other experiments in existing hypotheses
Alignment with your strategic priorities
You'll see AI suggestions as labels on experiment cards. You can accept these suggestions with one click or choose a different hypothesis.
The Hypothesis Management page provides a comprehensive view of all your testing theories:
Each hypothesis displays:
Hypothesis Statement - The core testing theory
Experiment Count - How many experiments are grouped under this hypothesis
Status Distribution - How many experiments are pending, approved, running, or completed
Results Summary - Aggregate performance data from completed tests
Validation Status - Whether the hypothesis is proven, disproven, or inconclusive
Hypotheses progress through several states:
Active - Currently being tested with experiments in progress
Validated - Multiple experiments have proven this hypothesis correct
Invalidated - Testing has disproven this hypothesis
Inconclusive - Mixed results require further testing or hypothesis refinement
Archived - No longer actively testing, but retained for historical reference
You can refine hypothesis statements based on learnings:
Update Description - Clarify or refine the testing theory
Adjust Priority - Change priority level based on results or strategic shifts
Merge Hypotheses - Combine related hypotheses that are testing similar theories
Split Hypotheses - Break a broad hypothesis into more specific ones
Using hypotheses transforms your review process from evaluating individual experiments to validating strategic theories:
Filter your review queue to show only experiments from a specific hypothesis. This allows you to:
Evaluate consistency across related experiments
Approve batches of experiments testing the same theory
Identify outliers that might need modification
Make strategic decisions about which hypotheses to prioritize
Develop approval criteria based on hypothesis validation:
High-Confidence Hypotheses - Quickly approve experiments from validated hypotheses
Testing Phase Hypotheses - Approve a diverse sample to test the theory
Invalidated Hypotheses - Consider rejecting new experiments or archiving the hypothesis
New Hypotheses - Review carefully until you establish confidence
The Hypothesis Analytics view aggregates results from all experiments within each hypothesis:
Overall Win Rate - Percentage of experiments that outperformed control
Average CVR Lift - Mean improvement across all tests in the hypothesis
Consistency Score - How uniform results are across experiments
Statistical Confidence - Aggregate confidence level for the hypothesis
Based on hypothesis performance, PressPlay provides actionable insights:
Validated Theories - Hypotheses with consistent positive results should inform future strategy
Invalidated Theories - Learn what doesn't work and avoid similar experiments
Refinement Opportunities - Hypotheses with mixed results may need more specific formulation
Emerging Patterns - Cross-hypothesis insights reveal deeper user behavior patterns
To get the most value from hypothesis grouping:
Start Broad, Then Narrow - Begin with broad hypotheses and refine them as you learn
Keep Hypotheses Distinct - Ensure each hypothesis tests a clearly different theory
Test Multiple Expressions - For each hypothesis, create diverse experiments that test the theory in different ways
Document Rationale - Write clear hypothesis descriptions explaining your reasoning
Review Results Holistically - Don't just look at individual experiments—analyze hypothesis-level patterns
Iterate Based on Learning - Update hypotheses as you gain insights
Share Knowledge - Use validated hypotheses to guide team strategy and decision-making
Hypothesis grouping transforms PressPlay from a testing tool into a strategic learning system. By organizing experiments around testable theories, you build a deeper understanding of your audience and develop a more sophisticated, data-driven approach to app store optimization.
