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相关概念视频

Decision Making: P-value Method01:09

Decision Making: P-value Method

5.3K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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相关实验视频

Updated: Jun 20, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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贝叶斯对最佳决策的信心.

Joshua Calder-Travis1, Lucie Charles2, Rafal Bogacz3

  • 1Department of Experimental Psychology, University of Oxford.

Psychological review
|July 18, 2024
PubMed
概括
此摘要是机器生成的。

漂移扩散模型 (DDM) 可以扩展到准确预测决策信心. 信任度反映了积累的证据强度,而决策时间受到惩罚,支持单个积累器模型.

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相关实验视频

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科学领域:

  • 认知心理学 认知心理学
  • 计算神经科学是一种神经科学.

背景情况:

  • 漂移扩散模型 (DDM) 准确地模拟决策和响应时间.
  • 目前基于DDM的信任模型有局限性,这促使人们探索扩展.
  • 替代决策模型经常被用于信心,尽管DDM的成功.

研究的目的:

  • 调查DDM的简单扩展,以更好地考虑决策信心.
  • 确定一个单一的证据积累过程是否可以为决策和信心提供信息.
  • 测试DDM框架是否可以调整以解释信任报告.

主要方法:

  • 开发并比较了几种DDM变体,包括信任.
  • 确保决策和信心依赖于相同的证据积累过程.
  • 根据基准数据和新的预先注册研究验证的模型.

主要成果:

  • 一个DDM变体的子集成功考虑了信心数据的定量方面.
  • 信心似乎反映了证据的强度受到决策时间 (贝叶斯式读取) 的惩罚.
  • 在信任报告中的时间罚款可能没有被完美校准.

结论:

  • 可以扩展DDM框架,以提供关于决策信心的强有力的说明.
  • 没有必要放弃DDM或单蓄电机模型进行信心研究.
  • 结果支持贝叶斯的信心读数,包括决策时间.