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相关概念视频

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Bonferroni Test01:10

Bonferroni Test

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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相关实验视频

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Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
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在多重等价性测试中改进了对家族智能的错误率控制.

Gwenaël G R Leday1, Jesse Hemerik1, Jasper Engel1

  • 1Wageningen University and Research, Biometris, Droevendaalsesteeg 1, 6708, PB, Wageningen, the Netherlands.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
|July 5, 2023
PubMed
概括

在测试许多特征时,食品安全等价性测试可能会导致错误的阳性结果. 像Adaptive Bonferroni这样的新方法比霍赫伯格的方法提供了比霍赫伯格的方法更强大的家庭错误率 (FWER) 控制,提高了安全评估的准确性.

关键词:
相当性测试是指同等性测试.家庭级错误 家庭级错误食品安全 食品安全多重测试 多重测试一种类型I错误的错误.

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科学领域:

  • 农业科学 农业科学
  • 统计科学 统计科学
  • 食品安全科学 食品安全科学

背景情况:

  • 对于食品和料安全性评估来说,等价性测试至关重要,通过证明作物特征的等价性,使得市场批准成为可能.
  • 当前每种分析物应用的单变量等价性测试缺乏多重性校正,在评估多个特征时增加I型错误率 (虚假的等价性声明).

研究的目的:

  • 评估和比较不同家庭错误率 (FWER) 控制方法在食品安全等效性测试中的功率.
  • 为了识别更强大的替代方案,霍赫伯格的方法管理在等价性评估多重比较.

主要方法:

  • 将霍赫伯格的方法与其他FWER控制程序进行比较,包括霍梅尔的方法和自适应的邦费罗尼方法.
  • 将这些方法应用于两个现实世界的组成数据集.
  • 使用模拟数据进行评估和比较,以评估各种场景下的性能.

主要成果:

  • 霍梅尔的方法被证明至少和霍赫伯格的方法一样强大.
  • 适应性Bonferroni方法,利用非等效特征的估计器,经常显示出比Hommel方法更大的实力.
  • 适应性Bonferroni方法在食品安全环境中特别有利,因为预计会有很高比例的真实等价值.

结论:

  • 在食品安全方面,标准的同等性测试实践可能由于未经纠正的多重性而过于保守.
  • 适应性Bonferroni和Hommel的方法在多分析剂等效性测试中为FWER控制提供了更好的统计能力.
  • 这些先进的方法提高了对新食品和料产品的安全评估的准确性和效率.