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Updated: Jan 16, 2026

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
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在二进制诊断测试中使用缩放逆概率重新抽样进行部分验证偏差校正

Wan Nor Arifin1, Umi Kalsom Yusof2

  • 1Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia.

PloS one
|September 26, 2025
PubMed
概括

新的方法,缩放的逆概率加权重新抽样 (SIPW) 和SIPW-B,减少偏差和标准错误的诊断准确性研究受影响的部分验证偏差 (PVB). 这些方法改进了反向概率引导 (IPB) 方法,以获得更可靠的测试评估.

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Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
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A complete procedure for testing a claim about a population proportion is provided here.
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Accuracy and Errors in Hypothesis Testing

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.
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Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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