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Questions and Answers
What does deductive mean?
What does deductive mean?
Premises are meant to logically entail the conclusion.
What does inductive mean?
What does inductive mean?
Premises are meant only to make the conclusion probable.
What are inductive arguments?
What are inductive arguments?
Arguments that are commonly used in science and daily life and make the conclusion probable.
Types of inductive argument/reasoning include __________.
Types of inductive argument/reasoning include __________.
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What is enumerative induction?
What is enumerative induction?
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What is the target group or population?
What is the target group or population?
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What does sample refer to in inductive reasoning?
What does sample refer to in inductive reasoning?
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What is a relevant property?
What is a relevant property?
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Common problems for enumerative inductions include __________.
Common problems for enumerative inductions include __________.
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What is a special case of enumerative induction?
What is a special case of enumerative induction?
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Bad or misleading questions can include __________.
Bad or misleading questions can include __________.
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What are statistical syllogisms?
What are statistical syllogisms?
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What is the general form of statistical induction?
What is the general form of statistical induction?
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What is the form of arguments from analogy?
What is the form of arguments from analogy?
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What is the subject in the context of arguments?
What is the subject in the context of arguments?
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What are the similarities in analogies?
What are the similarities in analogies?
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What is the target property in arguments from analogy?
What is the target property in arguments from analogy?
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Study Notes
Inductive and Deductive Arguments
- Deductive arguments entail conclusions logically derived from premises.
- Inductive arguments aim to make conclusions probable, focusing on observed instances.
Types of Inductive Reasoning
- Enumerative induction involves generalizing from specific observed instances to broader conclusions.
- Statistical syllogisms draw conclusions about individuals based on group characteristics.
- Arguments from analogy compare similarities between two entities to infer properties.
- Causal arguments establish a relationship of cause and effect.
- Inference to the best explanation infers the most compelling explanation based on the evidence presented.
Enumerative Induction
- Example: Observations of ravens being black lead to the generalization that all ravens are black.
- This method assesses observed instances to draw conclusions about unobserved instances.
Key Components of Inductive Arguments
- Target Group or Population: The overall group being generalized (e.g., all ravens).
- Sample: Observed instances that form the basis for induction (e.g., black ravens).
- Relevant Property: The attribute inferred from the sample to the target group (e.g., blackness).
Common Problems in Enumerative Inductions
- Small sample size can lead to inaccurate conclusions.
- Unrepresentative samples undermine the validity of generalizations.
- Poorly formulated questions can skew data and responses.
- Non-homogeneity in target groups regarding the property being projected complicates conclusions.
Opinion Polls and Representativeness
- For accurate results, a sample should mirror the target group in characteristics and proportions.
- Random sampling is the most effective way to ensure representativeness.
Issues with Question Quality
- Leading and loaded questions can bias responses.
- Vague questions may elicit unclear answers.
- Failure to provide response options can skew data.
- Double-barreled questions confuse respondents by combining multiple inquiries.
Statistical Syllogisms
- Involves drawing conclusions about an individual based on incomplete group knowledge.
- Example: If most members of a group share a characteristic, an individual from that group is likely to share it too.
Structure of Statistical Induction
- Premise 1: Majority of group characteristics
- Premise 2: Individual is part of that group
- Conclusion: Individual possesses that characteristic
Evaluation of Statistical Syllogisms
- Strength depends on the accuracy of the generalization and how typical the individual is within that group.
- Generalizations based on a higher percentage yield stronger conclusions.
Arguments from Analogy
- Analogical reasoning involves comparing similarities between two entities to infer characteristics of one based on the other.
- Essential components include the subject, analogue, similarities, and the target property.
Evaluating Analogical Arguments
- Assess the number and relevance of similarities and dissimilarities between the subject and analogue.
- Greater diversity among analogues supports stronger analogical arguments.
Example of Analogical Reasoning
- Comparison of androids and humans establishes that if humans feel pain, androids may also possess this capability based on shared similarities.
Strength of Arguments from Analogy
- The strength of the analogy increases with more instances, especially if they are diverse, as they represent a broader range similar to inductive reasoning.
Weak Analogical Reasoning
- Irrelevant similarities lead to weak conclusions, as seen in the example about book covers implying similar qualities falsely.
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Description
Explore key concepts of deduction and induction with this flashcard set. Understand the differences between deductive and inductive reasoning, as well as their applications in science and everyday arguments. Perfect for students looking to master logical frameworks.