:

Rosan Olsman1, Sarah Bonte2,3, Mattias Hofmans4,5

  • 1Laboratory Medical Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.

概括

CompensAID自动检测流细胞计数据中的参考错误,改善质量控制. 这种基于R的工具标记标记组合与潜在的不准确性,减少手动检查负担.

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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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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Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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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.
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Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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