In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, ... Definition · Error rate · Example · Application domains |
| Ошибки первого и второго рода |  |
Оши́бка пе́рвого ро́да — ситуация, когда отвергнута верная нулевая гипотеза.
Оши́бка второ́го ро́да — ситуация, когда принята неверная нулевая гипотеза.
Данные понятия являются ключевыми в задачах проверки статистических гипотез в математической... Википедия
18 янв. 2021 г. · In statistics, a Type I error is a false positive conclusion, while a Type II error is a false negative conclusion. Error in statistical decision... · Type I error · Type II error |
3 сент. 2024 г. · A type I error is an error that occurs when a null hypothesis is rejected, though it is true. Discover more about the type I error. What Is a Type I Error? · How It Works · Examples |
17 окт. 2022 г. · Type 1 errors are “false positives” – they happen when the tester validates a statistically significant difference even though there isn't one. |
In other words, a type 1 error is like a “false positive,” an incorrect belief that a variation in a test has made a statistically significant difference. |
In a test of hypothesis, a Type I error is committed when the null hypothesis is rejected despite being true. |
Videolar A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) ... |
5 окт. 2023 г. · Type I error. A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis. Simply ... |
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