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Related Concept Videos

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.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
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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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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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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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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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Drawing conclusions: Representing and evaluating competing explanations.

Alice Liefgreen1, David A Lagnado1

  • 1Department of Experimental Psychology, University College London, 26 Bedford Way, WC1H 0AP London, UK.

Cognition
|February 9, 2023
PubMed
Summary
This summary is machine-generated.

People evaluate legal explanations differently when asked to create causal models. Understanding causal mechanisms is key to assessing evidence in the criminal justice system.

Keywords:
Causal modelsEvidential reasoningExplanationMechanismSimplicity

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Area of Science:

  • Cognitive Science
  • Legal Psychology
  • Decision Science

Background:

  • Limited research exists on explanatory preferences within applied fields like criminal justice.
  • Previous studies focused on psychology and philosophy, neglecting practical applications.

Purpose of the Study:

  • To investigate explanatory preferences in the criminal justice system.
  • To determine if requiring causal model generation influences the evaluation of legal accounts.
  • To identify valued explanatory features, such as 'mechanism'.

Main Methods:

  • Participants evaluated competing legal explanations of evidence.
  • Causal model generation was manipulated as a condition.
  • Explanatory preferences and the perceived value of 'mechanism' were assessed.

Main Results:

  • Requiring participants to draw causal models significantly affected their evaluation of legal explanations.
  • The 'mechanism' was identified as a crucial feature people value in explanations.
  • This study is among the first to demonstrate the direct impact of causal model generation on explanation evaluation.

Conclusions:

  • Findings suggest that incorporating causal modeling can enhance legal argument analysis.
  • The results have implications for developing better legal reasoning tools and normative models.
  • This research expands understanding of the cognitive underpinnings of explanation evaluation in new domains.