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相关概念视频

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Random and Systematic Errors01:20

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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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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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Bias in Epidemiological Studies01:29

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Assessment of Child Anthropometry in a Large Epidemiologic Study
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在因果推理中的方法. 第三部分:测量错误和外部有效性威胁

Joseph A Bulbulia1

  • 1Victoria University of Wellington, New Zealand.

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概括
此摘要是机器生成的。

人文科学应该扩大样本范围,超越WEIRD社会,以实现概括性. 因果图揭示了测量错误和选择限制偏差是如何形成的比较研究,称为比较研究.

关键词:
因果推理的原因推理.斯威格斯 (Swigs) 的意思是说.这是很奇怪的.这是一个比较的比较.这是跨文化跨文化.时间 时间 时间 时间 时间实验 实验 实验 实验 实验在纵向的长度上.测量误差偏差 在测量误差偏差选择偏差是一种选择偏差.单一世界干预图表目标有效性的目标有效性.

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科学领域:

  • 人文科学 人文科学
  • 比较文化研究 文化比较研究
  • 因果推理的原因推理.

背景情况:

  • 超越西方,受过教育,工业化,富裕和民主 (WEIRD) 社会的广泛采样在伦理和科学上是可取的,以便在人文科学中进行概括.
  • 限制目标人群有时是必要的,但可以引入选择限制偏见,威胁到有效的因果推理.
  • 现有的统计测试,如不变性测试,无法解决比较文化研究中的结构性偏见.

研究的目的:

  • 澄清在人文科学中不受限制采样是可取或不可取的条件.
  • 使用因果图来识别测量误差和目标人口限制偏差的结构特征.
  • 将"奇怪"的研究定义为具有偏见的研究,威胁到对比文化研究中的因果推理.

主要方法:

  • 利用因果图来分析测量误差偏差和目标人口限制偏差.
  • 专注于对比较文化研究中有效因果推断的威胁.
  • 研究了统计不变性测试在解决结构偏差方面的局限性.

主要成果:

  • 因果图有效地澄清了因测量错误和选择限制而产生的结构偏差.
  • 定义"奇怪"的研究是那些由于不适当的限制和扭曲而错误估计的推断.
  • 证明了统计不变性测试无法解决这些结构偏差.

结论:

  • 对比文化研究需要仔细考虑采样策略,以避免偏见.
  • 因果推断工作流提供了安全有效的跨文化研究的必要检查清单.
  • 了解和减轻"奇怪"偏见对于推进人文科学中可概括的知识至关重要.