Statistical Experiment Target Population Selection System
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Solution Overview
Problem
Organizations face challenges in determining target populations for statistical experiments due to the lack of statistical knowledge among employees, requiring complex Boolean queries and manual input for identifying appropriate target populations.
Innovation Solution
A system utilizing processors and memory to automatically determine target populations by receiving hypotheses and target parameters, converting natural language prompts into Boolean queries, predicting sample sizes, and monitoring degradation metrics to end experiments when predetermined thresholds are exceeded, thereby streamlining the process for users with little to no statistical knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If employees manually input Boolean queries to determine target populations, then the accuracy of target population identification can be maintained, but the complexity of operation increases and usability deteriorates for users without statistical knowledge
Solution Approach 1:
The patent introduces an intermediary system that translates natural language inputs into Boolean queries. The system includes a natural language processing module that converts user-friendly language into the complex Boolean expressions needed for accurate target population identification, thereby maintaining precision while improving ease of operation
Solution Approach 2:
The system enables self-service by automatically generating and executing Boolean queries without requiring user expertise. The automated query generation module creates appropriate Boolean expressions based on simple user inputs, allowing non-experts to perform complex target population analysis independently
2Reliability
If employees manually set up and administer statistical tests, then the statistical rigor can be maintained, but the time consumption and productivity decrease
Solution Approach 1:
The patent applies preliminary action by pre-configuring statistical test templates and parameters before actual experiments. The system stores pre-validation statistical methodologies and automatically applies them to new experiments, ensuring statistical rigor is maintained while eliminating repetitive setup time
Solution Approach 2:
The automated statistical test execution system performs self-service by automatically selecting appropriate test types, setting parameters, and analyzing results without human intervention. This maintains statistical rigor through algorithmic consistency while dramatically improving productivity through automation
3Measurement precision
If the system provides detailed control over experiment parameters, then the measurement precision and statistical accuracy are improved, but the device complexity and difficulty of operation increase
Solution Approach 1:
The patent segments the experiment configuration process into distinct modular components: natural language input module, query generation module, statistical test selection module, and result analysis module. Each module handles specific aspects independently, reducing overall complexity while maintaining precision through specialized processing in each segment
Solution Approach 2:
The system implements universality by creating a multi-functional platform that handles natural language processing, Boolean query generation, statistical test execution, and result analysis within a single integrated system. This universal interface allows users to perform multiple complex functions through a unified simple interface, reducing perceived complexity while maintaining statistical accuracy
Data Source
AI summary
Systems and methods for automatically determining target populations for statistical experiments are disclosed. The system may receive a hypothesis associated with a statistical experiment and a target population, the hypothesis including one or more target metrics. The system may receive one or more target parameters associated with the target population. The system may determine whether the one or more target parameters match a stored query. In response to the target parameters matching the stored query, the system may query, using the stored query, a user database to determine the target population satisfying the target parameters. The system may predict a sample size for the statistical experiment based on the target population and the target metrics and transmit to the user device a graphical user interface including the predicted sample size.