CH 11: Inductive Logic and Reasoning

These flashcards cover key concepts of inductive logic and reasoning, focusing on inductive reasoning principles, arguments from analogy, statistical syllogism, casual hypotheses, and generalization techniques.

What is Inductive Reasoning?

Inductive reasoning is a logical process where generalizations are made based on specific observations or examples.

What is an argument from analogy?

An argument from analogy suggests that if two things are alike in one aspect, they are alike in other aspects as well.

What makes an argument from analogy stronger?

The more numerous and diversified the similarities between the premise-analogue and the conclusion-analogue, the stronger the argument.

What is a statistical syllogism?

A statistical syllogism applies a general statement to a specific case to draw a conclusion.

What is a causal hypothesis?

A causal hypothesis is a statement that suggests a cause-and-effect relationship that is subject to investigation.

What is the ‘paired unusual events’ principle in forming causal hypotheses?

If two unusual events happen simultaneously, one may be the cause of the other.

Define ‘Expectation Value’ (EV).

Expectation Value is the weighted average of all possible outcomes, factoring in the likelihood of each outcome.

What is the significance of a random selection process in scientific generalization?

A random selection process ensures every member of a population has an equal chance of being included in the sample.

What affect does sample size have on generalizations?

A larger random sample increases the likelihood that the sample proportion will be close to the true proportion.

How can one calculate probabilities of multiple independent events?

By expressing the probability of each independent event as a decimal and then multiplying those probabilities together.

What is the implication of the Gambler’s Fallacy?

The belief that the outcome of one independent event can affect the outcome of another independent event.

Discuss the consequence of hasty generalization.

Generalizing from a sample that is too small to accurately reflect the population leads to weak conclusions.

Why is it important to consider contrary premise-analogues in inductive reasoning?

The more contrary premise-analogues there are, the weaker the argument becomes.

What is meant by the term ‘confidence level’ in statistical sampling?

The probability that the random variation of a sample proportion will fall within the error margin.

What does the term ‘statistically significant’ indicate?

It indicates that a result is unlikely to have occurred by chance alone.