Control Test Method Quantization Reduces Sample Waste
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Solution Overview
Problem
Existing control test methods require large numbers of samples to ensure accuracy, leading to sample waste due to the need for multiple variables and controlled differences, which limits the number of samples available for testing.
Innovation Solution
The method involves quantization processing of experimental variables to obtain quantization indices, allowing for the estimation of an appropriate sample size based on evaluation indices, thereby determining the required number of samples needed for control testing, reducing sample waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of samples are selected for testing to ensure accuracy of control test results, then the reliability of experimental outcomes is improved, but the loss of experimental samples increases
Solution Approach 1:
The patent performs preliminary quantization processing on experimental variables and calculates the minimum sample size requirement before conducting the actual control test. By determining the adequate sample size in advance through mathematical modeling and evaluation indices, the system avoids both insufficient sampling and excessive sampling, thereby preventing sample waste while ensuring test accuracy.
2Reliability
If multiple variables are controlled to ensure experimental accuracy, then the reliability of results is improved, but the quantity of samples required increases
Solution Approach 1:
The patent segments the experimental variables into different categories (causal variables, control variables, outcome variables) and applies quantization processing to each category. This segmentation allows for systematic control of multiple variables while calculating the minimum sample size required for each variable type, thereby managing the total sample quantity efficiently without compromising experimental accuracy.
Solution Approach 2:
The patent transforms experimental variables from qualitative descriptions to quantitative parameters through quantization processing. By converting variables into numerical forms with defined evaluation indices, the system can mathematically determine the minimum sample size needed to achieve desired accuracy, replacing the traditional approach of using large samples as a blanket solution for controlling multiple variables.
3Reliability
If a large number of samples are used to find general laws, then the reliability of experimental conclusions is improved, but the productivity of the experimental process decreases
Solution Approach 1:
The patent performs preliminary quantization processing on experimental variables and calculates the minimum sample size requirement before conducting the actual control test. By determining the adequate sample size in advance through mathematical modeling and evaluation indices, the system avoids both insufficient sampling and excessive sampling, thereby preventing sample waste while ensuring test accuracy.
Data Source
AI summary
A control test method includes: obtaining an experimental scheme, and performing quantization processing on experimental variables in the experimental scheme to obtain a quantization index corresponding to each of the experimental variables, in which the experimental variables include a causal variable and an outcome variable; determining an evaluation index corresponding to the quantization index of the outcome variable, and determining, based on the evaluation index, a sample size corresponding to experimental samples in the experimental scheme; and obtaining the experimental samples under the sample size, and dividing the experimental samples into the experimental group and the control group based on a control grouping condition of the experimental scheme, in which the quantization index of the causal variable corresponding to the experimental group is different from the quantization index of the causal variable corresponding to the control group.