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Related Concept Videos

Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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Second-order probability affects hypothesis confirmation.

Katya Tentori1, Vincenzo Crupi, Daniel Osherson

  • 1Department of Cognitive Sciences and Education, University of Trento, Trento, Italy. katya.tentori@unitn.it

Psychonomic Bulletin & Review
|January 19, 2010
PubMed
Summary
This summary is machine-generated.

Human confirmation judgments are not strictly formal, as previously thought. Our study reveals that second-order probabilities influence how people assess evidence impact, deviating from purely formal Bayesian confirmation measures.

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Area of Science:

  • Cognitive Science
  • Decision Making
  • Bayesian Probability

Background:

  • Bayesian confirmation measures quantify the impact of evidence (E) on a hypothesis (H).
  • Existing measures are formal, depending solely on probabilities Pr(E), Pr(H), Pr(E|H), and Pr(H|E).
  • Prior research suggested human judgment might be formal within specific semantic domains.

Purpose of the Study:

  • To investigate whether human confirmation judgments are strictly formal.
  • To examine the role of second-order probabilities in assessing evidence impact.
  • To challenge the notion of formality in confirmation judgment, even within limited domains.

Main Methods:

  • Experimental design to assess human judgments of evidence impact on hypotheses.
  • Manipulation of second-order probability distributions over key probability values.
  • Comparison of human ratings against predictions from formal confirmation measures.

Main Results:

  • Human confirmation judgments are influenced by second-order probabilities, not just first-order probabilities.
  • When second-order probabilities are maximized, judgments align with formal confirmation measures.
  • Deviations from maximal second-order probabilities lead to systematically moderated evidence impact ratings.

Conclusions:

  • Human confirmation judgment is not formal; it incorporates second-order probability distributions.
  • The perceived impact of evidence is moderated by beliefs about the underlying probabilities.
  • This finding refutes the weaker version of formality and highlights a crucial aspect of human reasoning.