PHIL 252 — Unit 10: Putting Critical Thinking into Practice
The final unit integrates every tool covered in the course. It answers three questions: (1) How do we reason well in the real world? (2) What does a complete argument evaluation look like in practice? (3) How do we protect ourselves from bullshit in a data-driven world?
Learning outcomes:
- Apply course concepts to everyday critical thinking contexts
- Explain and reflect on approaches and attitudes about critical thinking
- Describe and evaluate arguments from real media
Part A — Chapter 20: Putting Critical Thinking into Practice (Dayton & Rodier)
20.1 Returning to Inductive Strength
Most real-world knowledge is merely probable, not certain. Inductive reasoning is limited because humans are not omniscient and cannot know the future. Our beliefs form a network of support relations — not isolated claims. The fact that beliefs are “merely probable” doesn’t mean you can believe whatever you like: beliefs are constrained by the entire network of things you know.
We organize beliefs into schemas and scripts (stories, goal-oriented plans, stereotypical situations). These schemas ground our inferences.
20.2 Material Inferences
Material inferences are defeasible inferences that depend on informal patterns — typical features, probable signs, reasonable assumptions. They are:
- Neither universal nor necessary
- Defeated by additional information
- Ineliminable from human reasoning
Four types: motivational (infer motive) · feature (infer from typical property) · resultative (infer consequence) · functional (infer purpose)
See MaterialInferences for the full breakdown.
20.3 The Three-Step Argument Analysis Procedure
| Step | What You Do | What You’re Testing |
|---|---|---|
| 1. Identify / Clarify / Distinguish | Put argument in standard form; make implicit premises explicit | Are the claims what they appear to be? |
| 2. Dialectical Acceptability | Are premises true, likely, and relevant? | PREMISES |
| 3. Logical Connection | Do premises give sufficient rational grounds? | CONNECTION between P and C |
See ArgumentAnalysisProcedure for full detail, cogency conditions, and the St. Albert Gazette worked example.
Cogency conditions (review):
- Premises rationally acceptable to a reasonable audience
- Premises make a rationally grounded connection to the conclusion
- Premises provide sufficient rational grounds
Walton’s 5 conditions for fallacies: argument · falls short of correctness · dialogue context · semblance of correctness · poses problem to the dialogue goal
20.4 Attitudes of Critical Thinking
- Fallacies are symptoms of bad reasoning — use them as diagnostic categories, not gotcha labels
- Be charitable: take even irritating arguments seriously as claims for reasonableness
- Taking cognitive ownership: evaluating an argument means taking it on as your own and asking whether you would endorse its structure
- Ask: is anything good in this argument? Can any parts survive if the fallacies are isolated?
Part B — Chapter 21: Fallacy Round-Up (Dayton & Rodier)
A complete reference table of all fallacy categories covered in the course.
21.1 Fallacies of Ambiguity (Ch. 14)
| Fallacy | Core Idea |
|---|---|
| Equivocation | Keyword used in two senses; argument depends on the shift |
| Amphiboly | Structural ambiguity in a sentence’s grammar |
| Accent | Meaning shifts depending on which word is stressed |
| Composition | From parts → whole (invalid); from member → class (invalid) |
| Division | From whole → parts (invalid); from class → member (invalid) |
| Hypostatization | Abstract word or metaphor treated as a concrete thing |
21.2 Fallacies of Emotional Bias (Ch. 15)
| Fallacy | Core Idea |
|---|---|
| Ad hominem (personal attack) | Reject claim by attacking the person |
| Abuse | Name-calling to redirect attention from the issue |
| Poisoning the well | Attack person’s motivation rather than their argument |
| Tu quoque | ”Look who’s talking” — person’s behaviour undermines their argument |
| Mob appeal | Sway belief via theatrical language or group-based interests |
| Appeal to pity | Evoke pity to cause assent |
| Appeal to force/fear | Threats of force to cause acceptance |
| Two wrongs make a right | Justify behaviour by asserting the other party would do the same |
21.3 Fallacies of Expertise (Ch. 16)
| Fallacy | Core Idea |
|---|---|
| Appeal to authority | Accept claim just because an expert says so (without evaluating the expertise) |
| Snob appeal | Believe this and you’ll join an exclusive superior group |
| Appeal to tradition | Past practice is reason enough to continue it |
| Appeal to nature | Natural = good; unnatural = bad |
| Appeal to anonymous authority | Claim supported by unspecified “experts” |
| Appeal to ignorance | Failure to disprove a claim = reason to accept it |
See FallaciesOfExpertise · AppealToAuthority
21.4 Fallacies of Distorting the Facts (Ch. 17)
| Fallacy | Core Idea |
|---|---|
| False analogy | Two things only superficially similar, or not similar in the relevant respect |
| False cause (family) | Insufficient evidence for a causal claim |
| Post hoc ergo propter hoc | B happened after A → A caused B |
| Mere correlation | B correlates with A → A caused B |
| Reversing cause and effect | A causes B when actually B causes A |
| Spurious correlation | A causes C when both are effects of B |
| Slippery slope | Event must inevitably lead to consequence — no argument for inevitability |
| Irrelevant thesis | Sidestep the real issue; claim it has been settled by the diversion |
See FalseCause
21.5 Fallacies of Presumption (Ch. 18)
| Fallacy | Core Idea |
|---|---|
| Sweeping generalization (accident) | Apply a general rule to a special case where it doesn’t apply |
| Hasty generalization (converse accident) | Build a general rule from a special/unrepresentative case |
| Bifurcation | Present a false choice; treat contraries as contradictories |
21.6 Fallacies of Evading the Facts (Ch. 19)
| Fallacy | Core Idea |
|---|---|
| Straw person | Distort opponent’s view to make it easy to knock down |
| Begging the question | Assume what you’re trying to prove |
| Question-begging epithets | Slanted language that implies what hasn’t been proved |
| Complex question | Question that presupposes the truth of what’s at issue |
| Special pleading | Apply slanted language to others; use neutral language for yourself |
See FallaciesOfEvadingTheFacts
Part C — Chapter 10: Spotting Bullshit (Bergstrom & West)
Six practical tools for identifying misleading claims, especially in media and social media contexts. See Bullshit for the expanded page.
| Tool | The Rule | Key Example |
|---|---|---|
| 1. Question the source | Who is telling me this? How do they know? What are they selling? | Goop healing crystals — questions reveal lack of real expertise |
| 2. Beware unfair comparisons | Entities must be directly comparable for the comparison to be valid | ”Most dangerous cities” lists — city boundary definitions skew rankings |
| 3. If too good/bad to be true → dig to the source | Social media amplifies extreme claims; the most viral posts are often those that shock | NBC tweet: “applications down 40%” vs actual report (applications down at 40% of schools) |
| 4. Think in orders of magnitude (Fermi estimation) | Make back-of-envelope approximations to check plausibility; being off by 50% still keeps you within tenfold | Food stamp fraud: 0.2% of budget lost, not a scandal; National Geographic 9 billion vs 9 million |
| 5. Avoid confirmation bias | Tendency to notice, believe, and share information consistent with preexisting beliefs | Letter-of-recommendation gender bias study: tweet showed the hypothesis, not the actual (mixed) results |
| 6. Consider multiple hypotheses | Just because someone has an explanation doesn’t mean it’s the explanation | Disney stock down 2.5% attributed to Roseanne cancellation — but the drop occurred before the announcement |
Online bullshit tactics:
- Corroborate and triangulate (check multiple sources)
- Pay attention to where information comes from
- Dig back to the origin; read the full story, not just the headline
- Use reverse image lookup (TinEye, Google Images)
- Be aware of deepfakes and synthetic media
- Use fact-checking sites: Snopes.com, PolitiFact.com, FactCheck.org
- Illusory truth effect: the more often you see something, the more likely you are to believe it → reduce information intake; “think more, share less”
Part D — Chapter 11: Refuting Bullshit (Bergstrom & West)
Three tools for calling out bullshit once you’ve spotted it.
| Concept | Definition | Example |
|---|---|---|
| Calling bullshit | A performative utterance in which one repudiates something objectionable; scope broader than bullshit alone (can call bullshit on lies, treachery, injustice) | Finding a reputable source that refutes a rumour; citing Snopes |
| Reductio ad absurdum | Refuting a claim by deriving an absurdity from its logical extension | Women’s running times: if the trend continued, women would eventually run 100m in 0 seconds |
| Null model | A model that shows what we would observe in a very simple system where not much is going on; test whether alternate explanations fit the same data | The fastest runner isn’t necessarily older — it’s the one in the largest sample; age effect disappears when sample size is controlled |
Part E — Chapter 11: Categorical Equivalence Review (Dayton & Rodier)
A review of immediate inference operations and their equivalences, with the concept of distribution made explicit.
Distribution
A term is distributed if the statement makes a claim about every member of the class referred to by that term.
| Statement | Subject distributed | Predicate distributed |
|---|---|---|
| A (All S are P) | Yes | No |
| E (No S are P) | Yes | Yes |
| I (Some S are P) | No | No |
| O (Some S are not P) | No | No* |
*Traditional logic distributes the predicate of O, but modern categorical logic takes neither term as distributed in O statements (to avoid existential import problems).
Equivalence Summary
| Operation | How | Equivalent for |
|---|---|---|
| Conversion | Switch S and P | E and I only |
| Contraposition | Switch S and P; negate both with “non-” | A and O only |
| Obversion | Change quality (all/no, some/some are not) + replace P with complement | All four (always valid) |
| Relation | Pair | Property |
|---|---|---|
| Contradiction | A↔O, E↔I | Opposite truth values; cannot both be true, cannot both be false |
| Contrary | A vs. E | Cannot both be true; can both be false (requires non-empty classes) |
| Subcontrary | I vs. O | Cannot both be false; can both be true (requires non-empty classes) |
| Subalternation | A→I, E→O | If universal is true, particular must be true (requires non-empty classes) |
Square of Opposition: Only contradiction holds in modern categorical logic. Contrary, subcontrary, and subalternation require the assumption that classes are not empty.
See ImmediateInference for the full breakdown and worked examples.
Key Distinctions for This Unit
| Concept | Key Distinction |
|---|---|
| Material vs. formal inference | Material = defeasible / informal; formal = necessary / valid |
| Confirmation bias vs. illusory truth effect | Bias = you seek confirming info; illusory truth = repeated exposure makes false things feel true |
| Calling bullshit vs. lying | Lying = knows the truth and contradicts it; bullshitting = indifferent to truth |
| Contrary vs. contradictory | Contradictory: one must be true; Contrary: both can be false |
| Dialectical acceptability vs. truth | DA = stands up to a reasonable audience; truth = corresponds to fact |
Mindmap
mindmap root((Unit 10<br/>Putting CT<br/>into Practice)) Material Inferences Motivational Feature Resultative Functional Defeasible 3-Step Analysis 1 Identify & Clarify 2 Dialectical Acceptability 3 Logical Connection Cogency Conditions Walton 5 Conditions Fallacy Round-Up Ambiguity Ch14 Emotional Bias Ch15 Expertise Ch16 Distorting Facts Ch17 Presumption Ch18 Evading Facts Ch19 Spotting Bullshit Question the Source Unfair Comparisons Too Good to Be True Fermi Estimation Confirmation Bias Multiple Hypotheses Refuting Bullshit Calling Bullshit Reductio ad Absurdum Null Model Categorical Equivalence Distribution Conversion E and I Contraposition A and O Obversion All Square of Opposition
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