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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.14514 |
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| _version_ | 1866908274225315840 |
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| author | Ursini, Edson Luiz Poletti, Elaine Cristina Catapani da Silveira, Loreno Menezes Leite, José Roberto Emiliano |
| author_facet | Ursini, Edson Luiz Poletti, Elaine Cristina Catapani da Silveira, Loreno Menezes Leite, José Roberto Emiliano |
| contents | The double hypothesis test (DHT) is a test that allows controlling Type I (producer) and Type II (consumer) errors. It is possible to say whether the batch has a defect rate, p, between 1.5 and 2%, or between 2 and 5%, or between 5 and 10%, and so on, until finding a required value for this probability. Using the two probabilities side by side, the Type I error for the lower probability distribution and the Type II error for the higher probability distribution, both can be controlled and minimized. It can be applied in the development or manufacturing process of a batch of components, or in the case of purchasing from a supplier, when the percentage of defects (p) is unknown, considering the technology and/or process available to obtain them. The power of the test is amplified by the joint application of the Limit of Successive Failures (LSF) related to the Renewal Theory. To enable the choice of the most appropriate algorithm for each application. Four distributions are proposed for the Bernoulli event sequence, including their computational efforts: Binomial, Binomial approximated by Poisson, and Binomial approximated by Gaussian (with two variants). Fuzzy logic rules are also applied to facilitate decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14514 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Acceptance or Rejection of Lots while Minimizing and Controlling Type I and Type II Errors Ursini, Edson Luiz Poletti, Elaine Cristina Catapani da Silveira, Loreno Menezes Leite, José Roberto Emiliano Methodology Artificial Intelligence Numerical Analysis The double hypothesis test (DHT) is a test that allows controlling Type I (producer) and Type II (consumer) errors. It is possible to say whether the batch has a defect rate, p, between 1.5 and 2%, or between 2 and 5%, or between 5 and 10%, and so on, until finding a required value for this probability. Using the two probabilities side by side, the Type I error for the lower probability distribution and the Type II error for the higher probability distribution, both can be controlled and minimized. It can be applied in the development or manufacturing process of a batch of components, or in the case of purchasing from a supplier, when the percentage of defects (p) is unknown, considering the technology and/or process available to obtain them. The power of the test is amplified by the joint application of the Limit of Successive Failures (LSF) related to the Renewal Theory. To enable the choice of the most appropriate algorithm for each application. Four distributions are proposed for the Bernoulli event sequence, including their computational efforts: Binomial, Binomial approximated by Poisson, and Binomial approximated by Gaussian (with two variants). Fuzzy logic rules are also applied to facilitate decision-making. |
| title | Acceptance or Rejection of Lots while Minimizing and Controlling Type I and Type II Errors |
| topic | Methodology Artificial Intelligence Numerical Analysis |
| url | https://arxiv.org/abs/2503.14514 |