Sequential Hypothesis Testing for Digital Marketing
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Solution Overview
Problem
Conventional fixed-horizon hypothesis testing in digital marketing environments is inflexible and inefficient, requiring a predetermined sample size and running until a set horizon is reached, which can lead to inaccurate results and increased time consumption, and does not allow for real-time monitoring or adjustment of parameters.
Innovation Solution
Sequential hypothesis testing allows for dynamic adjustment of sample size based on real-time data, enabling the test to conclude as soon as statistical significance is reached, allowing for real-time monitoring and flexible execution, reducing the number of user inputs, and using a false discovery rate to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed-horizon hypothesis testing is used with a predetermined sample size, then the test structure is simple and easy to implement, but the test cannot conclude early even when statistical significance is reached, leading to increased time consumption and opportunity costs
Solution Approach 1:
The patent transforms the static fixed-horizon testing approach into a dynamic sequential testing approach where the sample size is not predetermined but evolves based on accumulating data. The test continues sequentially, evaluating statistical significance at each step, and can conclude early when significance is reached, thus adapting the test duration dynamically rather than being fixed in advance.
Solution Approach 2:
The patent changes the parameter of sample size from a fixed predetermined value to a variable that increases sequentially. Instead of committing to a fixed horizon N, the testing process allows the sample size to grow step-by-step, enabling early termination when statistical significance is achieved, thereby reducing the expected sample size and test duration.
2Reliability
If fixed-horizon hypothesis testing runs until a set horizon is reached, then the predetermined sample size ensures sufficient data collection, but it increases the risk of missing smaller improvements and consumes unnecessary computational resources when significance is reached earlier
Solution Approach 1:
The patent introduces feedback mechanisms where the testing process continuously monitors statistical significance at each sequential step. This feedback allows the system to detect when sufficient evidence has been accumulated to reject the null hypothesis, enabling early termination. The feedback loop ensures reliability by maintaining error rate controls while avoiding unnecessary continued testing that would waste computational resources.
Solution Approach 2:
The sequential testing process is self-regulating, automatically determining when to terminate based on accumulated evidence. The system monitors its own data accumulation and statistical significance, making decisions about continuation or termination without external intervention. This self-service mechanism ensures that testing stops precisely when sufficient data has been collected, avoiding both premature termination and unnecessary extension.
3Ease of manufacture
If fixed-horizon hypothesis testing uses a predetermined sample size, then the test plan is straightforward to configure, but it requires manual specification of multiple parameters including confidence level, power, baseline conversion rate, and minimum detectable effect
Solution Approach 1:
The patent extracts and removes the need for users to specify multiple complex parameters such as confidence level, power, baseline conversion rate, and minimum detectable effect. By adopting a sequential testing framework with pre-controlled error rates, the system eliminates the burden of parameter specification while maintaining statistical rigor, thus simplifying the configuration process.
Solution Approach 2:
The sequential testing system is self-configuring in the sense that it does not require manual input of multiple statistical parameters. The error rate controls and stopping rules are built into the methodology itself, allowing the test to proceed with automatic determination of sample size and termination points without user intervention for parameter specification.
4Stability of the object's composition
If fixed-horizon hypothesis testing is used, then the test horizon is clearly defined in advance, but it does not allow for real-time monitoring or adjustment of parameters during the test execution
Solution Approach 1:
The patent transforms the static test plan into a dynamic process that can adapt during execution. While the error rate controls provide a stable framework, the actual sample size and termination point are determined dynamically based on accumulating data. This allows real-time monitoring of statistical significance and flexible adjustment of the testing duration without compromising the stability of the error rate guarantees.
Data Source
AI summary
Sequential hypothesis testing techniques are described, which involve testing sequences of increasingly larger number of samples until a winner is determined. In particular, sequential hypothesis testing techniques is based on whether a result of a statistic has reached statistical significance that defines a confidence level in the accuracy of the results. Sequential hypothesis testing also permits the user to “peek” into the test through use of a user interface (e.g., dashboard) to monitor the test in real time as it is being run. Real time output of this information in a user interface as a part of sequential hypothesis testing may be leveraged in a variety of ways. In a first example, a user may make changes as the test is run. In another example, flexible execution is also made possible in that the test may continue to run even if initial accuracy guarantees have been met.