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Related Concept Videos

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.
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Deductive Reasoning01:16

Deductive Reasoning

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
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Reasoning01:30

Reasoning

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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
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What are Estimates?01:06

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Theory of Attribution I: Correspondent Inference Theory01:15

Theory of Attribution I: Correspondent Inference Theory

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Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
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Induction01:16

Induction

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An emf is induced when the magnetic field in a coil is changed by pushing a bar magnet into or out of the coil. emfs of opposite signs are produced by motion in opposite directions, and the directions of emfs are also reversed by reversing poles. The same results are produced if the coil is moved rather than the magnet—it is the relative motion that is important. The faster the motion, the greater the emf. Additionally, there is no emf when the magnet is stationary relative to the coil.
A...
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Direct Inference and Probabilistic Accounts of Induction.

Jon Williamson1

  • 1Department of Philosophy and Centre for Reasoning, University of Kent, Canterbury, UK.

Journal for General Philosophy of Science = Zeitschrift Fur Allgemeine Wissenschaftstheorie
|January 24, 2022
PubMed
Summary
This summary is machine-generated.

This study challenges standard probabilistic theories of induction, arguing they cannot adequately explain inductive reasoning using direct inference principles. A novel non-standard objective Bayesian approach is proposed as a viable alternative for understanding induction.

Keywords:
BayesianismDirect inferenceInductionLogical probabilityPrincipal PrinciplePrinciple of the Narrowest Reference Class

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Area of Science:

  • Philosophy of Science
  • Epistemology
  • Logic

Background:

  • Probabilistic accounts of induction are criticized for failing to adequately explain inductive reasoning.
  • Schurz (2019) specifically critiques Bayesian approaches (subjective and objective) that rely on direct inference principles.
  • Existing direct inference principles, such as Reichenbach's and Lewis's, face challenges within standard probabilistic frameworks.

Purpose of the Study:

  • To evaluate the viability of standard probabilistic approaches to induction, particularly their use of direct inference.
  • To demonstrate the limitations of Reichenbach's Principle of the Narrowest Reference Class and Lewis's Principal Principle in standard probabilistic settings.
  • To propose and defend a non-standard objective Bayesian account of induction that accommodates direct inference.

Main Methods:

  • Critical analysis of Schurz's arguments against probabilistic accounts of induction.
  • Examination of the compatibility of direct inference principles (Reichenbach's and Lewis's) with standard probabilism.
  • Development and defense of a non-standard objective Bayesian framework for induction.

Main Results:

  • Standard probabilistic approaches, relying on direct inference, are shown to be inadequate for explicating the logic of induction.
  • Both Reichenbach's Principle of the Narrowest Reference Class and Lewis's Principal Principle present significant difficulties in standard probabilistic models.
  • A non-standard objective Bayesian account of induction is presented as a successful alternative, capable of incorporating direct inference.

Conclusions:

  • Direct inference principles face significant challenges within standard probabilistic frameworks for induction.
  • A non-standard objective Bayesian approach offers a promising and viable alternative for understanding the logic of induction.
  • The proposed non-standard account successfully addresses criticisms leveled against probabilistic theories of induction.