Practice worksheet

Deductive and Inductive Reasoning Worksheet: 12 Arguments with Answers

Mark the premises and conclusion, then test the support offered. These twelve original arguments include valid deductions, failed guarantees, sample-based estimates and a case with missing information. Each answer explains the reasoning; two download files keep the questions and answer key separate.

Beginning philosophy students, independent learners and teachers · 20 minute read · Published

How to use the worksheet

For each case, identify the premises (P1, P2 and so on) and conclusion (C); state the offered support: a deductive guarantee, an inductive estimate/prediction, or insufficient information; then explain whether that support succeeds. For a failed guarantee, describe a situation with all premises true and the conclusion false. For an estimate, name the sampling or stability issue. All cases and numerical records are fictional.

Try the cases before opening the answers. Write more than a label: a correct label with a reversed conditional still misses the reasoning. Phrases such as “the organizer offers an estimate” explicitly set the classroom task; they do not claim that every ordinary argument has an obvious, uncontroversial classification.

Download the twelve questions without answers · Download the explained answer key

Keep support, validity and truth separate

Four checks for an argument
CheckQuestion to askMistake to avoid
Premises and conclusionWhich claims are offered as reasons, and which claim are they meant to support?Treating every sentence as a premise, including the conclusion.
Deductive validityCould all the premises be true while this conclusion is false?Calling an invalid guarantee inductive merely because it fails.
Truth and soundnessAre the premises actually true, as well as the argument valid?Assuming that a valid form verifies the records or facts.
Inductive supportHow well do the stated sample, model or pattern support this particular prediction?Assuming that any remaining possibility of error makes all estimates equally weak.

For this sheet, a deductive attempt offers a guarantee; an inductive estimate or prediction offers support that can leave room for error. The offered guarantee can fail. The estimate can be weak. This is a working classroom convention: Timothy Shanahan’s Internet Encyclopedia of Philosophy article explains why strict classifications, intentions and indicator words are disputed.

A counterexample to validity keeps every premise true and makes the conclusion false. Saying “the badge might be invalid” is not enough in case 2 unless permission and the conditional rule can still hold. By contrast, a possible exception to an inductive prediction shows that it is not a guarantee; evaluating its strength also requires inspecting the evidence. Case 7 and case 8 have different sampling quality despite both allowing error.

The twelve arguments

Case 1

A fictional archive promises a guarantee. P1: Every document in Cabinet A is catalogued. P2: Document L is in Cabinet A. C: Document L is catalogued.

Show answer and explanation for case 1

Deductive; valid. P1 and P2 are the premises; C is the conclusion.

If both premises are true, L cannot be an uncatalogued document in that cabinet. The inference applies a universal rule to one member. That establishes validity, not whether the archive actually keeps its promise. To call the argument sound, you would also need true premises. Finding a different uncatalogued document would challenge P1, rather than this inference form.

Case 2

A fictional venue argues that entry proves a badge is valid. P1: If Jo has a valid badge, entry is permitted for Jo. P2: Entry is permitted for Jo. C: Jo has a valid badge. The venue presents C as guaranteed.

Show answer and explanation for case 2

Deductive attempt; invalid. The form affirms the consequent.

P1 provides one sufficient condition for permission, not the only condition. Counterexample: Jo has no valid badge but has an authorized visitor pass. Entry is permitted, so P2 is true; the valid-badge rule remains true, so P1 is true; C is false. This tests the connection between the premises and conclusion. It does not assert that a real venue uses these rules.

Case 3

In a fictional device, P1: If the heater is on, its indicator is lit. P2: The heater is not on. C: The indicator is not lit. A technician claims the premises guarantee C.

Show answer and explanation for case 3

Deductive attempt; invalid. The form denies the antecedent.

The rule does not say that only the heater can light the indicator. Imagine the heater is off while an indicator-test function lights it. Both premises are true and C is false. A different premise, “The indicator is lit only if the heater is on,” would block that counterexample, but it is absent. Do not repair the argument silently by reversing its conditional.

Case 4

A fictional studio offers a guarantee. P1: All senior members are registered members. P2: All registered members have signed the studio agreement. C: All senior members have signed the studio agreement.

Show answer and explanation for case 4

Deductive; valid, although both premises and the conclusion are general.

Any senior member is registered by P1 and has signed by P2. A senior member who had not signed would violate at least one premise. This defeats the shortcut that deduction must always move from a general rule to a particular person. Whether the membership and signature records make P1 and P2 true is a separate factual check.

Case 5

The fictional inventory shows exactly two single-colour tokens in Box K: one red and one blue. An argument nevertheless claims a guarantee. P1: Every token in Box K is red. P2: The blue token is in Box K. C: That blue token is red.

Show answer and explanation for case 5

Deductive; valid in form, but unsound in the supplied fictional inventory.

The form is the same as case 1: every K is R; this token is K; therefore it is R. Its validity concerns what would follow if both premises were true. Here P1 is false: the inventory includes a blue, non-red token. P2 is true and C is false in that inventory. A false conclusion does not, by itself, prove an invalid form; a false premise can explain it.

Case 6

A fictional class uses a complete, closed roster. P1: The only registered students are Ari, Bo and Cy. P2: Ari has submitted the form. P3: Bo has submitted the form. P4: Cy has submitted the form. C: Every registered student has submitted the form. The teacher presents a guarantee.

Show answer and explanation for case 6

Deductive; valid because the roster is explicitly complete.

P1 closes the population: there cannot be another registered student left unexamined. P2–P4 cover every member, so C follows. The move from named individuals to a general conclusion does not make this induction. If P1 merely said that Ari, Bo and Cy are three registered students, an additional student who had not submitted would be a counterexample. The word “only” does important work.

Case 7

In a fictional club of 1,000 members, 100 members are selected at random without replacement and all answer the same clearly worded question. P1: Ninety of those 100 prefer a morning meeting. C: Probably most club members prefer a morning meeting. The organizer offers a sample-based estimate, not a guarantee.

Show answer and explanation for case 7

Inductive; the stated sampling procedure provides support for the population estimate.

The conclusion concerns 900 members not surveyed. A random sample with complete responses is a better basis than asking only convenient friends, but its result does not logically require a population majority. The unasked members could, in principle, mostly prefer evenings. Check sampling, response and question wording before generalizing. No numerical confidence level is calculated here, and 90% in the sample is not an exact population result.

Case 8

Another organizer asks only five friends from the club’s evening study group. P1: All five prefer evening meetings. C: Probably most of the 1,000 club members prefer evening meetings. The organizer offers a sample-based estimate.

Show answer and explanation for case 8

Inductive; weak support for this club-wide conclusion because the sample is narrow and biased.

The respondents were chosen from a group whose schedule already favors evenings. Counting their agreement does not remove that selection problem. A counter-situation is a large morning-preferring membership alongside these five evening-preferring friends: P1 remains true while the club-wide majority claim is false. That possibility alone does not refute all induction; the specific selection mechanism explains why this estimate is poorly supported.

Case 9

A fictional bag contains exactly 100 single-colour tokens: 90 red and 10 blue. P1: Those are all its tokens. P2: One token will be drawn, with each token equally likely. C: The drawn token will probably be red. This is a prediction of the outcome.

Show answer and explanation for case 9

Inductive prediction; strongly supported under the stated draw model, but not guaranteed.

The model gives red a probability of 90/100, or 0.9. Nevertheless, any one of the ten blue tokens could be drawn while both premises remain true. A blue outcome would therefore not show that the stated probability model was contradicted. The conclusion predicts a colour, rather than calculating a probability; compare case 11, which uses the same bag for a different claim.

Case 10

A fictional commuter records a particular weekday service. P1: Its last 20 departures were on time. C: Its next departure will probably be on time. The commuter uses the record as a forecast.

Show answer and explanation for case 10

Inductive; a past pattern supports a tentative prediction, with important missing conditions.

C reaches beyond the departures actually recorded. Past punctuality can be relevant, but it gives no guarantee about the next departure. A changed timetable, maintenance or disruption could matter without falsifying P1. The statement supplies no method for estimating the next departure’s exact probability. A careful answer names the pattern and the missing stability assumption, rather than turning 20 successful observations into certainty.

Case 11

Use the same fictional 100-token bag as case 9. P1: Exactly 90 tokens are red and 10 are blue. P2: Each token has the same chance in one draw. C: The probability of drawing red is 9/10. The calculation is presented as guaranteed by these premises.

Show answer and explanation for case 11

Deductive; valid within the stated finite, equally likely probability model.

There are 90 favorable tokens among 100 equally likely possibilities: 90/100 = 9/10. This conclusion states a probability, rather than insisting that the actual token must be red. A blue draw does not make the probability claim false. A tampered draw could instead challenge P2. Probability vocabulary does not automatically make an inference inductive; examine exactly what conclusion follows from which premises.

Case 12

You find only this note: “The meeting-room door is closed. Therefore, the meeting has begun.” The note gives no venue rule, observation record or explanation of the intended strength of support. P1: The door is closed. C: The meeting has begun.

Show answer and explanation for case 12

Insufficient information for a confident intended classification or a well-supported conclusion.

As written, P1 does not entail C: an empty, closed room is a counterexample to a deductive guarantee. No frequency evidence is supplied for judging a probabilistic connection. A strict rule that the door is closed only during meetings would supply one possible deductive reconstruction; a reliable observed pattern could supply an inductive one. Neither premise is stated. Record the gap and ask what connects a closed door to a meeting.

Four shortcuts these cases challenge

  1. “General to particular always means deduction.” Case 4 is a valid general-to-general deduction; case 9 predicts a particular outcome from a general distribution. Case 6 is a valid deduction from a complete enumeration.
  2. “Probably means induction.” Compare the outcome prediction in case 9 with the probability calculation in case 11. The two conclusions say different things.
  3. “A false conclusion means an invalid argument.” Case 5 has a valid form and a false premise. Validity alone cannot correct an inaccurate inventory.
  4. “A stronger-sounding word fixes weak support.” The promised guarantees in cases 2 and 3 still fail. A guarantee must be earned by the premise–conclusion connection.

For your next practice example, underline the conclusion’s exact scope: one person, a whole population, the next event or a numerical probability. Small changes can alter what support is required. Keep the original claim in your notes before proposing a better argument.

Notes for teachers and study groups

A useful discussion asks for three things: the premise–conclusion separation, the chosen standard and a reason that tests that standard. Invite a second person to challenge a proposed counterexample: does it accidentally make a premise false? Cases 2 and 3 are particularly useful for this check.

Pair cases 7 and 8 to discuss selection rather than just sample size. Pair cases 9 and 11 to distinguish an outcome from its probability. For case 12, accept a careful statement of missing information rather than rewarding an invented hidden premise. If your course uses a different definition of deductive or inductive arguments, state that convention and explain any resulting label change.

The question download includes writing prompts; the separate answer key supports offline review. These are original teaching examples, not survey findings, transport statistics or tested learning outcomes. The educational aim is to make the grounds for an answer inspectable.

Sources and further reading