Distinguish inductive from deductive reasoning and assess inductive strength across generalisation, analogy and inference to the best explanation

What this dot point is asking

SEAB wants you to distinguish inductive from deductive reasoning and to evaluate inductive arguments by their strength. Most real reasoning in science, history and everyday life is inductive: the premises support the conclusion without guaranteeing it. Because induction does not aim at certainty, it is assessed differently from deduction, and confusing the two standards is a common error in the critical thinking paper.

The answer

Induction versus deduction

A deductive argument claims its conclusion follows necessarily from its premises; if the premises are true, the conclusion cannot be false. An inductive argument claims only that its premises make the conclusion probable. The hallmark of induction is that the conclusion can be false even when the premises are true and the reasoning is good. Induction is ampliative: the conclusion goes beyond the information in the premises, which is why it can be informative but never certain.

Strength, not validity

Inductive arguments are not valid or invalid; they are strong or weak, and strength comes in degrees. An inductive argument is strong if its premises, were they true, would make the conclusion highly probable, and weak otherwise. A strong inductive argument whose premises are actually true is called cogent, the inductive counterpart of a sound deductive argument. So the evaluative pair for induction is strength and cogency, mirroring validity and soundness for deduction.

Enumerative generalisation

The most familiar inductive form generalises from observed cases to a wider claim: every observed sample of a substance behaved thus, so the substance generally behaves thus. Its strength depends on the size and representativeness of the sample. A large, varied, randomly drawn sample yields a strong generalisation; a small or biased sample yields a weak one and risks the fallacy of hasty generalisation.

Argument by analogy

An analogical argument infers that because two things are alike in some respects, they are alike in a further respect. Its strength depends on the number and relevance of the similarities and the absence of relevant differences. Analogy is powerful for generating hypotheses but easily abused: a single striking similarity rarely supports a strong conclusion if the relevant differences are large.

Inference to the best explanation

A third form reasons from a body of evidence to the hypothesis that best explains it: the patient has these symptoms, and the diagnosis that best accounts for them is X, so probably X. Its strength depends on how much better the favoured explanation is than its rivals, judged by criteria such as explanatory scope, simplicity, and fit with background knowledge. This form is central to science and to detective-style reasoning, and it underlies the scientific-method debates in the sciences area.

Examples in context

Example 1. Polling and sample quality. A poll of two thousand randomly selected, demographically balanced voters supports a reasonably strong inductive generalisation about how the electorate leans. A poll of two hundred people recruited outside one political party's rally supports a weak one, because the sample is unrepresentative. The contrast shows that inductive strength turns on the quality of the sample, not merely its existence.

Example 2. Diagnosis as inference to the best explanation. A doctor weighs a patient's symptoms and test results and selects the diagnosis that best explains them, ruling out alternatives that fit less well. The reasoning is inductive: the diagnosis is probable, not certain, and a better-fitting explanation could emerge. This is the everyday face of inference to the best explanation, the same pattern that scientists use to choose between theories.

Try this

Q1. Explain why an inductive argument can have true premises and a false conclusion without being a bad argument. [6 marks]

• Cue. Induction is ampliative and claims only probability, so even a strong argument with true premises can have a false conclusion; that does not make the reasoning faulty, since it never promised certainty.

Q2. State two factors that make an enumerative generalisation strong. [6 marks]

• Cue. A large sample and a representative (unbiased, varied) sample; small or skewed samples produce weak generalisations and risk hasty generalisation.

Q3. Explain what inference to the best explanation is and one criterion for judging which explanation is best. [8 marks]

• Cue. It reasons from evidence to the hypothesis that best accounts for it; criteria include explanatory scope, simplicity, and fit with background knowledge, judged against rival explanations.