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相关概念视频

Goodness-of-Fit Test01:16

Goodness-of-Fit Test

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The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
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Inductive Reasoning00:59

Inductive Reasoning

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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Social Proof00:52

Social Proof

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Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.
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Cause and Effect01:53

Cause and Effect

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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相关实验视频

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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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信息和解释的好处信息和解释的好处

David H Glass1

  • 1School of Computing, Ulster University, York St, Belfast, BT15 1ED UK.

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PubMed
概括
此摘要是机器生成的。

这项研究引入了贝叶斯的框架来评估解释性的良性. 它提出,好的解释将相对于解释性收益的复杂性成本降到最低,使用特设假设作为例子.

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相关实验视频

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科学领域:

  • 科学哲学的科学哲学
  • 贝叶斯的认识论贝叶斯的认识论.
  • 科学解释 科学解释

背景情况:

  • 当前贝叶斯账户主要集中在确认上.
  • 缺乏一个量化衡量解释性的好处.
  • 临时假设对于评估解释来说是一个挑战.

研究的目的:

  • 提出一个定性贝叶斯对解释性善良的解释.
  • 引入一个复杂性标准来评估解释.
  • 用临时假设来说明这个帐户.

主要方法:

  • 开发一个类似于增量确认的贝叶斯框架.
  • 根据复杂性标准 (解释性收益与成本) 定义解释性的好处.
  • 应用框架来分析临时假设.

主要成果:

  • 如果解释的复杂性降低 (收益) 超过其引入的复杂性 (成本),解释被认为是好的.
  • 拟议的账户提供了解释能力的定性评估.
  • 该框架为分析临时假设提供了一种新的方法.

结论:

  • 定性贝叶斯学说为解释性的善良提供了一个新的视角.
  • 复杂性标准为评估科学解释提供了一个明确的指标.
  • 这个框架可以扩展到科学推理中的各种背景.