Online Experiment Termination via Alpha Spending Function
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Solution Overview
Problem
Existing online testing platforms face inefficiencies in controlling Type I error and maintaining statistical power due to frequent monitoring of results, leading to potential incorrect decisions and resource wastage, especially when experiment duration is fixed and not adaptable to changing data patterns.
Innovation Solution
A modified combination of the always valid p-value (AVP) process and the alpha spending function approach is implemented, generating adjusted p-values at discrete times to control Type I error while maintaining a constant significance level, allowing for early experiment termination without inflating Type I error, and providing user-friendly interfaces by displaying fixed alpha values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If frequent monitoring of experiment results is performed, then early termination capability is improved, but Type I error control deteriorates
Solution Approach 1:
The system implements periodic monitoring at discrete time points rather than continuous monitoring. An alpha spending function allocates the significance level across predetermined time points, allowing results to be assessed at specific intervals while maintaining overall Type I error control. This periodic approach enables early termination when significance is achieved without the harmful effects of continuous monitoring.
Solution Approach 2:
The experiment design pre-specifies monitoring time points and the alpha spending allocation before the experiment begins. By predetermined the schedule of when results will be assessed and how the significance level will be distributed across these time points, the system prevents data-driven decisions about when to monitor, thereby maintaining statistical validity while enabling early termination.
2Reliability
If fixed experiment duration is used, then Type I error control is improved, but productivity deteriorates
Solution Approach 1:
The system transforms the static fixed-duration experiment design into a dynamic framework where the experiment can terminate early if significance is achieved. The alpha spending function allows the significance level to be spent over time, enabling the experiment to adapt its duration based on the emerging data patterns while maintaining overall Type I error control at the predetermined alpha level.
Solution Approach 2:
The system changes the parameter of experiment duration from a fixed value to a flexible parameter that can be adjusted based on statistical significance. By using the alpha spending function to allocate significance level across time, the effective duration becomes a variable that depends on when the cumulative effect reaches statistical significance, thereby improving efficiency without sacrificing reliability.
3Loss of time
If continuous monitoring is implemented, then early termination is improved, but resource consumption increases
Solution Approach 1:
The system replaces continuous computational monitoring with periodic assessment at predetermined time points. By only calculating and evaluating results at specific intervals defined by the alpha spending function, the system reduces unnecessary computational operations while still maintaining the ability to detect significance early and terminate the experiment when appropriate.
Data Source
AI summary
The present disclosure generally relates to systems and methods for generating termination notification of an experiment to be presented through a user interface. In some implementation examples, a termination notification system generates a probability value that is valid as of a time period of an experiment at a predetermined point in time based on at least data sample obtained during the time period, predicted sample sizes of each time period of the experiment, or probability values generated prior to the time period. Responsive to determining that the probability value that is valid as of the time period satisfies a threshold value, the termination notification system causes the user interface to present the termination notification that, when selected, causes the experiment to terminate prior to completion of a predicted duration of the experiment.