Critical Thinking Study Guide
Return to Critical Thinking
Mid-term covers 1st, 2nd, and part of 4th disciplines and associated readings in handbook and reader.
Note: On concepts be sure to be able to identify, define, and explain the significance of each concept.
1st Discipline: Reflective Voice
- Five Disciplines of Thought -- know all five, by heart!
1. reflective voice 2. reconstruction 3. critical response 4. recognizing knowledge 5. seeing complexity
- Terms: Rationales, arguments, explanations, claim, premise, conclusion, reflective/deliberative context. (p. 3)
• Rationales: any speech or writing which includes at least one reason for a related conclusion • Arguments: reasons offered for a conclusion to help decide whether to believe the conclusion; doubt in the conclusion • Explanations: reasons offered for a claim helps understand how some fact or situation came to be; no doubt in the conclusion but not positive in the claims • Claim: every reason, premise, or conclusion • Premise: a proposition supporting or helping to support a conclusion • Conclusion: a proposition concluded or inferred from the premises of an argument • Reflective/deliberate context: situations in which rationales are discussed or thought about
- Thinking in Stereo: what is it, what questions are asked at each level.
“Thinking about thinking” The mind thinking, the object being thought about, and my thoughts about “how I am thinking about the object” Am I focusing on the right aspects or issues? Am I reasoning clearly about the topic? When does a way of thinking provide a good reason for believing something? When am I justified in expressing absolute certainty about something?
- Cognitive Bias: 1 Anchoring, 2 Framing, 3 Fundamental attribution error, 4 Confirmation bias,
a. Anchoring- overvaluing a specific piece of information b. Framing- how you “set up” or “frame” a situation will have an effect on how it is perceived by others c. Fundamental attribution error- natural tendency to explain people’s behavior using dispositional or intentional terms and concepts rather than situational ones d. Confirmation bias- tendency to look at new information in a way that fits our preconceptions about what we already think must be true
- Thought Experiment for finding reflective ideals: What do you need to count on when you begin a serious discussion with someone? What specific values and expectations should one have? What mutual obligations follow? p. 12 and following.
- Reflective ideals: sympathetic understanding, seeking knowledge, inviting appraisal.
- Necessary conditions for reflective exchange - beyond understanding eachothers language. Must be oriented toward truth and seeking knowledge. Sympathetic understanding - understanding a view before critically assessing it. Where is the person coming from? Seeking knowledge - imagine you could be wrong. Evaluate the knowledge claim you are making. Inviting appraisal - Genuinely considering what is said.
Questions on Readings:
- Haidt: How does basic information about the human brain help us thinking about the nature of thought?
Basic information about the human brain helps us think about the nature of thought because it enables us to incorporate left vs right brain and old vs new brain thinking. The four ideas are so different, especially old vs new, that it helps one think about concepts in nature.
- Stanovich: Look at specific thinking "puzzles" Stanovich consider, but also try to state his general point.
Baseball vs Bat, US Department of Transportation, XYZ Syndrome, New Medical Treatment, and Even-Vowel Cards are examples.
- Gopnik: How does Gopnik want us to think about thinking? What's her evidence?
2nd Discipline: Reconstruction
- Theory of Rationales - basic defintion of a rationale, distinction between argument and explanation.
- a. Rationale – using one claim to support another
- b. Argument – used to support a belief in the truth of the rationale
- c. Explanation – used to explain how the conclusion came about, conclusion is not in question
- Distinguishing argument and explanation (skill of identification from exercise set "Distinguishing Argument from Explanation).
- 3 Criteria for Good Reconstruction.
- a. Identify rationales
- b. Create a logical structure
- c. Make a fair interpretation
- Reconstruction (skill) Might have a short argument to reconstruct. (Not Fall 2010)
- Distinguishing Deductive and Inductive arguments. (skill) also, give definitions and compare. (Handbook topic: "Logical Structure in Deductive and Inductive Reasoning")
- a. Deductive – truth of conclusion with absolute certainty; has true premises and a valid structure
- b. Inductive - find conclusion through probability. Relys on patterns and certainties of nature.
- How do you show logical structure in deductive arguments? in inductive? in explanation? (Handbook topic: "Deductive Argument Forms" "Inductive Argument Forms", and "Form in Explanations".)
- a. Deductive –
- -Modus Ponens – if ‘P’ cause ‘Q’, and ‘P’ happens, then ‘Q’
- -Modus Tollen – if ‘P’ cause ‘Q’, and ‘Q’ doesn’t happen, then not ‘P’
- b. Inductive –
- -Patterns to conclusion
- Inductive relys on observed regularities. Create an inference based on a claim of experience.
- c. Explanation. Arise out of doubt. Plausible explanations involve a verifying experience (recall the cheating boyfriend and his 'cousin'). Essentially a causal story that raises your confidence in the conclusion.
- Identify and give examples of basic deductive argument forms and formal fallacies.
If Tim is a new student, he should expect to feel confused. Tim is a new student. Therefore, Tim should expect to feel confused. Formal Fallacies-Patterns that look valid but are not. Ex P-->Q `P >those three, also known as Denying the Antecedent `Q
P--> Q >those three, also known as Affirming the Consequent P
P-->Q R-->Q >those three, also known as Undistributed Middle P-->R
- Validity. (esp. relation to truth.) Can a valid argument have a false conclusion? In a valid argument is the conclusion always true?
- a. Validity – true structure, makes sense
- - Can have valid argument but false conclusion
- Conditional guarantee - "if premises are true, conclusion must be true."
- Basic inductive patterns and inductive analogies.
- a. Inductive patterns – inferences, relations; general to specific
- b. Analogies – set of particular resemblances between two things that are otherwise dissimilar
- Understand discussion of "Why Mars is Red" in "Form in Explanation"
- a. Planet covered in iron oxide; shows water reacting with planet’s iron in rocks, explains empty channels [weakness: we don’t know if there was an abundance of water]
- b. Using data from space missions, planets soil full of iron from meteorites; Albert Yen replicated ‘growth’ of oxide ions using UV radiation [strength: doesn’t need proof of presence of water]
- c. Neither is strong enough to prove the posed question, but together forms a strong reason; NASA rover supported both hypothesizes
- Gladwell: Why is it so hard to offer cross cultural explanations of people's drinking behavior?
3rd Discipline: Critical Response
- 3 Techniques for assessing rationales. (skill)
- What is critical response?
- What is the difference between assessing rationales and giving a critical response?
- Ad hominem fallacy
- What factors should you consider in preparing a critical response to someone's rationales?
- Identify some of the ways that critical response discussions can wrong.
- Questioning the truth of the premise. Questioning the connection b/t premise and conclusion. And Reframing the Issue.
- A critical response is an assessment of the rationales that seeks to understand the goals of the reflective context. What is the main argument/explanation?
- see above. Critical responses look for a goal. Defines specific arguemnets or explanations. Takes on a burden of proof.
- Ad hominem - attacking the person as a means of discrediting the views.
- Be willing to provide reasons for any view I put forward. have sesitivity to how others feel about having their views critically assessed.
- Misjudging goals. Failure to connect. There are many example stories in the text.
4th Discipline: Recognizing Knowledge
- What does it mean to call some information authoritative in the everyday sense? in the academic sense?
- What is the "peer review" process and how does it contribute to the recognition of knowledge?
- What does is mean to define knowledge as "justified, true belief"?
- What is the difference between "knowledge by discovery" and "knowledge by interpretation"?
- Trustworthy source with authoritative credentials. Everyday sense is someone whom you trust to give you accurate info. Academic sense-see peer review.
- Peer review-professional peers review argumetns before they are published. Knowledge claims must pass expectations of those already established as an authoritative source. Knowledge is agreed upon.
- Justified - able to give a coherent account of confidence in its truth. Articulated belief.
- Discover - "uncovering" truth or pattern in our experience. Interpretation - already known, just giving a distinctive interpretation.
- Causation: what is it. What did Hume say about it? How did Mill prepare the way for modern statistics?
- Concepts in causation and correlation: independent variable, explanatory variable, dependent variable, response variable, strength of correlation, direct and inverse relationships, linear regression and multiple regression analysis.
- Causation answers a "why" question. Hume says- you dont see a cause, you infer them. It is an expereince of the reliable connection b/t two events. "pairing" of events. Causal knowledge discloses the necessary or sufficient conditions for effect. Mill - noticed correlation. There is a variation in cause and effect.
- independent - the variable having causing the effect. Same as explanitory variable. Variable explaining why something happens. (study time)
- dependent - variable being effected. same as response variable. Shows a response. (test scores)
- strength of correlation - how random or predictable the pairing of variables is. (does study time have a correlation to hours studied)
- direct relationship- their is a direct effect on the dependent variable. It is not random (study time does directly effect how well you do on tests)
- inverse relationship - eg. the more you study the worse you do.
- linear regression - strong correlation between two variables. (creates a line)
- multiple regression analysis - tests for correlation among multiple factors.
- remember - correlation isnt causation!
Rich, "For Whom the Cell Tolls"
- Why is it hard to determine whether cell phones cause brain tumors?
Schick & Vaughn, "Science and It's Pretenders"
- Give a general characterization of science based on this reading.
- What point is being made by the author's claim that, "All of the data upon which the atomic theory rests, however, can be described without mentioning atoms."
- Learn the story of Benjamin Franklin and blind testing.
- Study the criteria of adequacy for scientific hypotheses.
- What does it mean to say that we only test scientific hypotheses in bundles?
- What is phlogiston?
- Quantitative Information in Knowledge Claims
- 1 What is a measure?
- 2 Baseline
- 3 Percentages and rates
- 4 Linear vs. Non-linear relationships
- 5 Surveys
- 6 Probability
- 1 Definition,
- 2 Gambler's fallacy,
- 3 Predictive dreams
- 4 SI jinx
- 7 Causation
- 1 Regression analysis
- 2 Multiple regression analysis
- Measure - quantification of some aspect of our experience. Numeric representation in "data point"
- Baseline - standard of comparison for interpreting change. Helps understand relative size of change.
- Examples - tuition rate increase.
- Percentages and rates - % change is a ration of part to whole (part to 100). Rate change is the % change of the % change.
- Linear vs non - Linear change in x produces proportional change in y. Constant proportion. Non-linear proportion changes at different points.
- Example- taking asprin every day vs every other day. Not every increment of change will have the same effect.
- Surveys - Public opinion polls. Good survey has representative sampling. Every relevant difference in sample has an equal chance of appearing in total population.
- Definition - ration of number of ways that a particular event can occur over the number of possible outcomes for the event. Frequentist probability is determined through experience. eg. death by plane crash.
- Gamblers Fallacy. 'Fallacy of Division' expectation that the probability will even itself out. eg. if there are multiple heads flipped in a row one might assume that a tails is due.
- Predictive Dreams. Illustrates 'Law of Large Numbers' If probability of predictive dream is x, then after a large number of nights, your empirical record of predictive dreams will approach x. Similar to coin flips - will approach 50/50 after a large number of flips.
- SI Jinx. Bell curve attributed to superstitions. In order to know if jinx is hoax, you need enough evidence to know if fluctuations were out of the ordinary. Athletes on covers of SI were located at the right of the bell curve, so there is a high probability that the next measure of performance will be lower. Due to 'Regression toward mean' Movement toward center of bell curve.
- I didnt see causation in the reader. May just not have been looking hard enough. Ths is where independent vs dependent variable come into play.
Silberman, "Placebos are Getting Better"
- Why does Silberman think placebos are getting better?
- What explanations are offered for this phenomenon?
Groopman, "The Plastic Panic"
- Summarize the main evidence for and against the claim that BPA is a dangerous chemical
Sevilla, "Probability" Chapters 18 and 19
- Major concepts: random process, sample space, event, sampling with/without replacement, probability, disjoint events, conditional probability, independent events.
- Skills: Determine the probability of an event from a table of data, determine the conditional probability of an event from a two-way table.
5th Discipline: Seeing Complexity
- 1. Simplification as part of knowledge production
- 2. Systems, complex systems, chaotic systems (links, nodes, degrees of separation)
- 3. Coupling, buffering, feedback loops
- 4. degrees of separation
- 5. Konigsburg bridge problem
- 6. Baltimore syphilis epidemic, Colorado Springs G. epidemic
- 7. What do good managers of complex systems do?
- - make more decisions per goal
- - Realize that a small change is going to have a large effect
- - Test hypothesizes
- 8. Thin slicing and the return of intuition
- 9. Stereotyping