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相关概念视频

Hindsight Biases01:12

Hindsight Biases

3.4K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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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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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Reliability and Validity01:29

Reliability and Validity

12.7K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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相关实验视频

Updated: Jul 11, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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当专家预测失败的时候

Igor Grossmann1, Michael E W Varnum2, Cendri A Hutcherson3

  • 1Department of Psychology, University of Waterloo, Waterloo, N2L 3G1, ON, Canada.

Trends in cognitive sciences
|November 10, 2023
PubMed
概括
此摘要是机器生成的。

社会科学家擅长实验室预测,但由于过于简单化的模型,他们与现实世界的社会变化作斗争. 将基础模型与时间序列数据集成,可以提高社会科学预测的准确性.

关键词:
有关因果关系的模型.专家的判断 专家的判断分析的分析水平.建模复杂性的复杂性社会科学中的预测能力.社会的变化是社会变化.

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A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
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相关实验视频

Last Updated: Jul 11, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
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科学领域:

  • 社会科学 社会科学 社会科学
  • 预测科学科学 预测科学

背景情况:

  • 在社会科学中,专家判断对于做出预测至关重要.
  • 当前的预测准确性在受控实验室环境和复杂的现实社会现象之间有很大差异.

研究的目的:

  • 仔细检查社会科学预测中专家判断的机会和挑战.
  • 确定当前社会科学因果模型的局限性,并提出改进方案.

主要方法:

  • 在社会科学中分析现有的因果模型.
  • 实验室与现实环境中的预测准确性的比较.
  • 与物理科学和气象学的预测方法进行并行.

主要成果:

  • 社会科学家在基于实验室的预测中表现出高于偶然的准确性.
  • 在预测现实世界的社会变化方面存在重大挑战.
  • 常见的因果模型往往过于简单化,与现象不一致,或缺乏考虑更广泛的因素.

结论:

  • 过于简单的因果模型阻碍了准确的社会变化预测.
  • 建议采用综合方法,将基础模型与时间序列数据结合起来.
  • 呼吁更加精确,雄心勃勃的预测和增加社会科学中的智力谦卑.