Inductive Logic·Intermediate·5 lessons·~280 min

Inductive Logic: Evidence, Generalization, and Causal Support

How to reason well under uncertainty

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What you'll learn

By the end of this unit, you can…

Assess inductive strength.
Formalize generalization.
Evaluate analogical argument.
Critique causal claim.

Lessons

Lesson sequence

1
What Makes an Inductive Argument Strong?
Introduces inductive strength, uncertainty, and defeasibility, and establishes the three-question routine students will use throughout the unit.
15 activities5 worked examples
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2
Generalization and Sample Quality
Teaches students how to formalize sample-based arguments, evaluate sample quality, and recognize the common species of sampling failure.
15 activities5 worked examples
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3
Analogical Reasoning
Teaches how to evaluate arguments from analogy by separating relevant similarities from superficial ones and identifying disanalogies that can block the inference.
15 activities5 worked examples
Student Pro
4
Causal Inference and Mill's Methods
Teaches the difference between correlation and causation, introduces Mill's methods for causal inference, and develops a rival-factor analysis routine students can apply to real causal claims.
15 activities5 worked examples
Student Pro
5
Capstone: Evaluating Inductive Arguments in the Wild
An integrative lesson that asks students to run the full inductive cycle on arguments drawn from research, journalism, and everyday claims: identify the inductive structure, assess sample quality and causal rivals, and calibrate the strength of the conclusion.
2 activities5 worked examples
Student Pro

How to study

Three moves that work for this unit

Read the explanation

Each lesson opens with a guided walkthrough — read it before the activity.

Study the worked example

Look at why each step follows, not just what the answer is.

Practice with the target in mind

Know which rule applies and what would make the response weak before you start.

Reference materials

Optional context for the unit. Each lesson surfaces the concepts and rules it uses — these are here when you want the bigger picture.

Concept map (7 terms)

Inductive Strength

The degree to which premises make a conclusion probable or well-supported without guaranteeing it.

Defeasibility

The feature of an argument whose support can be weakened or defeated by new evidence.

Representativeness

The extent to which a sample reflects the broader population it is used to support claims about.

Sample Size

The number of observed cases in the evidence base from which a generalization is drawn.

Analogical Reasoning

An inference that supports a conclusion about one case because it is relevantly similar to another case.

Causal Inference

Reasoning that moves from evidence to a claim about what caused a given outcome.

Confounding Variable

A third factor that influences both the supposed cause and the supposed effect, producing a correlation that does not reflect direct causation.

Rules and standards (4)

Sample Quality. A broader and more representative sample usually supports a stronger generalization, and projection should not exceed what the sample warrants. Common failures: The sample is too small for the claim's scope.; The sample is biased by self-selection or convenience sampling.; The target population is much broader than the evidence allows..
Relevant Similarity. An analogical argument is stronger when the similarities cited are relevant to the conclusion and when important disanalogies are accounted for. Common failures: The similarities are superficial and not connected to the feature being projected.; Important differences between the source and target cases are ignored..
Correlation Is Not Yet Causation. A causal conclusion requires more than noticing that two things occur together; rival explanations must be considered and ruled out. Common failures: A causal claim is drawn directly from a correlation.; Confounders, reverse causation, and coincidence are ignored.; A single case is treated as proof of a general causal pattern..

Inductive Logic: Evidence, Generalization, and Causal Support

By the end of this unit, you can…

Lesson sequence

What Makes an Inductive Argument Strong?

Generalization and Sample Quality

Analogical Reasoning

Causal Inference and Mill's Methods

Capstone: Evaluating Inductive Arguments in the Wild

Three moves that work for this unit

Read the explanation

Study the worked example

Practice with the target in mind

Reference materials

Inductive Strength

Defeasibility

Representativeness

Sample Size

Analogical Reasoning

Causal Inference

Confounding Variable

Mastery requirements

Assessment advice