Critical Inquiry - Philosophy Stuff

Critical Inquiry - Philosophy Stuff

CRITICAL INQUIRY P A RT T H R E E CHAPTER 2 OBJECTIVES Students will learn to: Recognize complications regarding premises and conclusions Distinguish between deductive and inductive arguments Understand the standards for validity, soundness, strength, and weakness in arguments Assess an argument with an unstated premise Distinguish between ethos, pathos, and logos Identify a balance-of-considerations argument Identify an inference to the best explanation (IBE) Use techniques for understanding arguments CHAPTER 2 Arguments: General Features

Introduction Examples of Arguments Conclusions Used as Premises (Extended Argument) Unstated Premises & Unstated Conclusions Two Kinds of Arguments Deductive Arguments Good Deductive Argument Valid Argument Invalid Argument Sound Argument Unsound Argument CHAPTER 2 Inductive Arguments Inductive Arguments Strong Argument

Weak Argument Uses of Inductive Arguments Generalization Analogy Past to Future Causal Reasoning Recap: The Basic Types of Arguments Deductive: valid/invalid, sound/unsound Inductive: strong/weak Beyond a Reasonable Doubt Deductive Proof CHAPTER 2

Deduction, Induction & Unstated Premises Introduction Deductive or Inductive Reconstruction Reconstruction CHAPTER 2 Balance of Considerations and IBES Balance of Considerations Reason Inference to the Best Explanation Deductive: Valid or invalid? Inductive: Strong or weak? What are Not Premises, Conclusions or Arguments Introduction Pictures

Ifthensentences Lists of Facts A because B CHAPTER 2 Ethos, Pathos and Logos Aristotles Theory of Persuasion Ethos Pathos Logos Techniques for Understanding Arguments Introduction Steps Distinguishing arguments from window dressing What is the author trying to prove?

What reasons are given? CHAPTER 2 Evaluating Arguments Introduction Do the premises support the conclusion? Deductive: Valid or invalid? Inductive: Strong or weak? Are the premises reasonable? Guidelines Credible source, observations, background information, credible claims. Conflict Vague, ambiguous, unclear. CHAPTER 2 RECAP Arguments consist of a premise (or premises) and a conclusion. The same claim can be a premise in one argument and a conclusion in a second argument.

The two fundamental types of reasoning are deductive demonstration and inductive support. A deductive argument is used to demonstrate or prove a conclusion, which it does if it is sound. An argument is sound if it is valid and its premise (or premises) is true. An argument is valid if it is impossible for its premise (or premises) to be true and its conclusion to be false. An inductive argument is used to support rather than to demonstrate a conclusion. Support is a matter of degrees: An argument supports a conclusion to the extent its premise (or premises) makes the conclusion likely. An argument that offers more support for a conclusion is said to be stronger than one that offers less support; the latter is said to be weaker than the former. , compatibility with well-accepted explanations, and freedom from unnecessary assumptions. CHAPTER 2 RECAP Some instructors use the word strong in an absolute sense to denote inductive arguments whose premise (or premises) makes the conclusion more likely than not. Inductive arguments and deductive arguments can have unstated premises.

Whether an argument is deductive or inductive may depend on what the unstated premise is said to be. If the argument you are contemplating is one someone has offered you, and you are having trouble tracking the part of an argument that appears in a written passage, try diagramming the passage. Balance of considerations reasoning involves deductive and inductive elements. If considerations are compared quantitatively, weighing them involves deductive reasoning. Predictions as to outcomes involve inductive reasoning. Inference to best explanation is a common type of inductive reasoning in which one tries to determine the best explanation for a phenomenon by comparing alternative hypotheses in terms of their explanatory adequacy, predictive accuracy CHAPTER 10 OBJECTIVES Students will learn to: Identify and differentiate statistical syllogisms, inductive generalizations from samples, and inductive arguments from analogy Explain the Principle of Complete Evidence in inductive reasoning

Define and explain the key terms related to samples and sampling Differentiate between scientific generalizing from samples and everyday generalizing from samples Apply the two principles of evaluating everyday generalizations from samples Analyze analogies and analogues Identify informal indicators of confidence levels and error margins Understand and identify various fallacies related to induction CHAPTER 10 Introduction Inductive Arguments Defined Strong/Weak Relative Strength & Relative Probability Relative strength of the argument/probability of the conclusion. Principle of Total Evidence Additional Information

Makes the conclusion more likely. The original argument is neither stronger nor weaker. CHAPTER 10 Arguing from the General to the Specific (Statistical Syllogism) Form of an Inductive/Statistical Syllogism Premise 1: Such-and such Xs are Ys Premise 2: This is an X Conclusion: Therefore this is a Y. Real World Syllogisms Assessment The higher the % of Xs that are Ys, the stronger the argument. Other factors might affect the probability that a specific X is Y. CHAPTER 10 Arguing from the Specific to the General (Inductive Generalization) Determining What %of Xs are Ys

Samples Form of an Inductive Generalization Premise 1: P% of observed Xs are Ys Conclusion: P% of all Xs are Ys. Terms Sample N Target/Target Class/Target Population (all Xs) Feature/Property in Question (Y) Sample Frame How likely? Target Population & Feature Sampling Frames

CHAPTER 10 Bias & Representative The strength depends on whether Y/X in the sample = Y/X in the population. Representative sample. Random Sample Error Margin & Confidence Level Sample Size Confidence Level of 95 CHAPTER 10 Sample Size Error Margin (%)

Corresponding Range (percentage points) 10 +/- 30 60 25 +/- 22 44 50 +/- 14 28

100 +/- 10 20 250 +/- 06 12 500 +/- 04 8 1,000

+/- 03 6 1,500 +/- 02 4 CHAPTER 10 Everyday Generalizing from a Sample Everyday generalizing differs from scientific generalization Samples Variable Representative Assessing Samples

Size Diversity Bias Homogeneous Two Basic Principles for Assessing samples A difference that biases a sample weakens the argument. Samples that are too small or undiversified weaken the argument. Examples CHAPTER 10 Reasoning from the Specific to the Specific: Inductive Arguments from Analogy Introduction The Way Inductive Arguments from Analogy Work Form Premise 1: X and Y both have properties P, Q, R Premise 2: X has feature F.

Conclusion: Therefore Y has feature F. Example Concepts Analogues Probabilities Not about gauging the probability of the conclusion. Principle of Total Evidence CHAPTER 10 Assessment Relative strength Relevant similarities & differences The more diversified the similarities, the stronger the argument. The more diversified the differences, the weaker the argument. Contrary analogue Attacking the Analogy Guidelines for thinking critically about an argument from

analogy The more numerous and diversified the similarities, the stronger the argument. The more numerous and diversified the differences, the weaker the argument. Examples CHAPTER 10 Other Uses of Analogies Analogies Moral & Legal Analogies The principle of relevant difference Explanations Historical Analogies Logical Analogies CHAPTER 10 Reasoning from General to General Summary

Reasoning from the general to the specific: statistical syllogism. Reasoning from the specific to general: inductive generalizing from samples. Reasoning from the specific to the specific: inductive arguments from analogy. Reasoning from the General to the General Drawing a conclusion about one population by considering the attributes of another. An argument from analogy using populations. Examples CHAPTER 10 3 KINDS OF INDUCTIVE ARGUMENTS Illicit Inductive Conversions Conversion Deduction The Form

Premise 1: __ Xs are Ys Conclusion: Therefore __Ys are Xs The blank is filled in with percentages or terms implying percentages. Deductive categorical logic & conversion Inductive logic & conversion Examples Examples Example: medical tests CHAPTER 10 Informal Error-Margin and Confidence Level Indicators Introduction Confidence level

Informal confidence level indicator phrases Informal error margin indicator words. More on Confidence Levels Estimation of Probability Estimation Matching error margin and confidence level indicators to the size and representativeness of the sample. CHAPTER 10 Fallacies in Inductive Reasoning Fallacy of Hasty Generalization A generalization based on a sample that is too small to be representative. The fallacy arises from overestimating the strength of the argument based on a small sample. Examples Fallacy of Anecdotal Evidence Drawing a conclusion from an anecdote about one or a very small number of cases.

Overestimating the strength of the argument based on overestimating Ignoring data that supports a general claim in favor of an example or two that runs against the evidence. Examples Fallacy of Biased Generalization/Analogy Basing a generalization/analogy on a biased sample. Overestimating the strength of an argument based on a nonrepresentative sample. Examples CHAPTER 10 The Self Selection Fallacy Self-selected sample Self-selection fallacy: estimating the probability of a conclusion derived from a relatively large but self-selected sample. Examples Person on the street interviews, telephone surveys, and questionnaires. Slanted Questions

Ways of asking Sequence No opinion Loaded questions Push polling CHAPTER 10 Weak Analogy Overestimating the probability of a conclusion derived from an argument from analogy. Weak/poor/false analogy: the analogues in an analogical argument are too dissimilar to justify the inference from one to another. Vague Generalities

A general statement that is too vague to be meaningful. Examples Testable by specifying a sample frame Glowing generality Opposite of a glowing generality CHAPTER 10 RECAP Inductive reasoning is used to support a conclusion rather than to demonstrate or prove it. Inductive arguments can be depicted as relatively strong or relatively weak, depending on how much their premises increase the probability of the conclusion. The strength of an argument is distinct from the overall probability of the conclusion. You can have a relatively strong argument for a conclusion whose overall probability is very low, and a relatively weak argument for a conclusion whose overall probability is quite high. Statistical syllogisms have the form: Most Xs are Ys; this is an X; therefore this is a Y.

The strength of a statistical syllogism is distinct from the probability of its conclusion everything considered. The latter depends on The Principle of Total Evidence. The former depends on the proportion of Xs that are Ys. Everyday inductive generalizations from samples differ from scientific inductive generalizations from samples in that everyday samples are not scientifically selected to eliminate bias, and probabilities in everyday generalizing cannot be calculated precisely. Thinking critically about everyday generalizations from samples involves the two principles stated on page 355. Inductive reasoning from analogy is based on the idea that things alike in some respects will be alike in further respects. Thinking critically about inductive arguments from analogy involves the principles stated on page 365. The time-honored strategy for rebutting an argument from analogy is to attack the analogy by calling attention to important dissimilarities between the analogues. CHAPTER 10 RECAP Arguments from analogy are especially important in ethics, history, and law, and to refute other arguments. We can support a conclusion about one population by reasoning analogically from a second population that has similar attributes.

Page 383 An overestimation of the strength of an argument based on a small sample is hasty generalization. An overestimation of the strength of an argument based on a biased but notso-small sample is biased generalization. The fallacy of anecdotal evidence is a version of hasty generalization in which the sample is presented as a narrative. Generalizations based on anecdotes are often persuasive psychologically, even though they are based on a sample of one. The self-selection fallacy is a version of biased generalization in which the sample is self-selected. When we overestimate the probability of a conclusion derived from an argument from analogy, we commit the fallacy called weak analogy. Vague generalizations suffer not so much from lack of support as from lack of substantive meaning. CHAPTER 11 OBJECTIVES Students will learn to:

Differentiate between arguments and explanations Recognize two important types of explanations Apply standards for evaluating explanations Apply methods for forming causal hypotheses Learn methods for confirming causal hypotheses Recognize mistakes in causal reasoning Distinguish the concept of cause as it applies to law CHAPTER 11 CAUSAL ARGUMENTS Introduction Explanations & Arguments Arguments Explanations Arguments & Explanations

Two Kinds of Explanations Physical causal explanations Examples Physical background Complication Adequate Behavioral causal explanations Examples Behavioral

Not fully predictable Future Mistake: reason for vs. a persons reason for CHAPTER 11 CAUSAL ARGUMENTS Explanatory Adequacy: A Relative Concept Introduction Adequate explanations cannot be: Self contradictory Vague Ambiguous Incompatible with established facts/theories Lead to false predictions

The Importance of Testability Predictions Nontestable Explanations Circular Explanations CHAPTER 11 CAUSAL ARGUMENTS Unnecessary Complexity Adequate explanations should: Be consistent Not conflict with established fact/theory Be testable Not be circular Avoid unnecessary assumptions/complexities.

Forming Hypotheses Introduction Hypothesis Forming a hypothesis and testing an hypothesis Inference to the best explanation CHAPTER 11 CAUSAL ARGUMENTS The Method of Difference The method Examples Hypothesis confirmation The Method of Agreement Correlation Associated events Cause Covariation The Method Correlation

Cum hoc, ergo propter hoc (with that, therefor because of that) Post hoc, ergo propter hoc (after this, therefore because of this) CHAPTER 11 CAUSAL ARGUMENTS Causal Mechanism s & background Knowledge The method Examples Hypothesis confirmation The Best Diagnosis Method Finding a causal hypothesis

Best Diagnosis Method General Causal Claims Introduction Specific & General Causal Claims Specific casual claim General causal claim X causes Y in population P: there would be more cases of Y in P if every member of P were exposed to Y than if no member of P were exposed to X. CHAPTER 11 CAUSAL ARGUMENTS Confirming Causal Hypotheses Introduction Controlled Cause-to-Effect Experiments Process Concepts Frequency & Statistical Significance CHAPTER 11

CAUSAL ARGUMENTS Number in Experimental Group (with similarly sized control group) Approximate Figure That d Must Exceed To Be Statistically Significant (in percentage points) 10 40 25 27 50 19

100 13 250 8 500 6 1,000 4 1,500 3 CHAPTER 11

CAUSAL ARGUMENTS Alternative Methods of Testing Causal Hypotheses in Human Populations Nonexperimental Cause-To-Effect Studies Definition Difference from a controlled cause-to-effect experiment Experimental group is not exposed to the suspected causal agent, C. Are exposed to C by their own actions or circumstances. Causal agent Difference The experimental group is randomly selected from individuals who Have already been exposed to C. Self-selection & Bias Inherently weaker than controlled experiments. CHAPTER 11 CAUSAL ARGUMENTS Nonexperimental Effect-To-CauseStudies Definition: to test whether something is a causal factor for an

effect. Difference from a controlled cause-to-effect experiment The experimental group displays effect E. The control group does not display the effect. Causal agent The experimental group might differ in important ways . Probably frequency of the cause, not the effect. Animal Testing Mistakes in Causal Reasoning Reasons to Reject Causal Explanations Unduly complicated Incompatible with known facts and theories Vague, ambiguous, or circular Inherently untestable.

CHAPTER 11 CAUSAL ARGUMENTS Post Hoc, Ergo Propter Hoc Defined Form P1: As immediately precede Bs (or this A precedes this B). C: Therefore, As cause Bs (or this A causes this B). Cum Hoc Ergo Propter Hoc Defined Form P1: As are correlated with Bs. C: As cause Bs. Why These are Fallacies They do not establish the improbability of three possibilities Possibility 1: The connection between A and B is coincidental Possibility 2: Both A and B result from a third thing (an underlying cause) Possibility 3: B caused A, rather than A causing B (reversing cause

and effect) CHAPTER 11 CAUSAL ARGUMENTS Confusing Conditional Probability in Medical Tests The probability that X given Y is distinct from the probability of Y given X. Testing positive for a condition is the effect of that condition, not the cause. Knowing the actual chance of having the condition Example Known symptoms of a condition Overlooking Statistical Regression Statistical regression/regression to the mean Examples More examples Proof by Absence of Disproof Absence of disproof

Disprove Absence of disproof is not proof. CHAPTER 11 CAUSAL ARGUMENTS Appeal to Anecdote Defined Examples Establishing a Causal Factor Confusing Explanations with Excuses Not all explanations are intended to be excuses Fallacy of Confusing Explanations & Excuses. Justification Explanation vs. Justification

Causation in the Law Harm Conditio sine qua non (a condition without which nothing) But for: Y would not have happened but for Xs having happened. Punish Indefinitely CHAPTER 11 CAUSAL ARGUMENTS Legal/Proximate Cause Severe Restrictions on Sine Qua Non Sine qua non cause vs. legal proximate cause H.L.A. Hart and A.M. Honore Legal responsibility

Intervening forces Coincidence CHAPTER 11 RECAP 1. Explanations are different from arguments. They are used to elucidate a phenomenon; arguments are used to support or prove a claim. 2. Sentence that can be used as explanations can also be used to state the conclusion of a premise of an argument. 3. Explanations serve a variety of purposes. Two important purposes are (1) to provide physical causal explanations of something and (2) to provide behavioral causal explanations of something. 4. What counts as an adequate explanation is relative to ones purposes and needs. 5. An adequate explanation shouldnt be unnecessarily complicated, inconsistent, incompatible with known fact or theory, or untestable due to vagueness, circularity or other reasons. 6. Arriving at a causal hypothesis involves an inference to the best explanation. 7. Methods of arriving at causal hypotheses are the Method of Difference, the Method of Agreement and the Best Diagnosis Method.

CHAPTER 11 RECAP 8. These methods are guided by ones background knowledge of causal mechanisms, what causes what and how things work. 9. Confirming a causal hypothesis consists primarily in rigorously applying a combination of the Methods of Difference and Agreement. 10. Two important mistakes in causal reasoning are post hoc, ergo propter hoc, and cum hoc, ergo propter hoc. 11. These are mistakes because they do not eliminate the possibility of coincidence, an underlying cause, or confusion between cause and effect. 12. An important case of confusing effect and cause is forgetting that symptoms are effects. 13. Changes due to statistical regression are sometimes mistakenly assumed to be due to causation. 14. Absence of disproof of causation is not equivalent to proof of causation. 15. Using an anecdote to establish causation or to refute a general causal claim involves hasty generalizing. 16. Explanations of bad behavior are not always intended to excuse bad behavior. 17. In the law, in its broadest sense, a cause is that but for which an effect would not have happened.

Recently Viewed Presentations

  • Hydrocarbons and Fuels

    Hydrocarbons and Fuels

    What to do Add a weighing boat to the balance and zero it Add between 2.2 - 2.5 g zinc oxide Record the accurate mass Measure out 25cm3 1.0 mol l-1 H2SO4 Heat the acid to about 50 oC Add...
  • PowerPoint 프레젠테이션 - Yonsei University

    PowerPoint 프레젠테이션 - Yonsei University

    - The Kerr effect - Electrorefraction. using a reverse-biased contact - Acoustooptic. Bragg diffraction. Two advantages of Si guided-wave optical circuits - Monolithic optoelectronic integration with high-speed Si electronic circuit - Utiliztion. in optics of well developed processing techniques from...
  • Runtime LOD - University of Wisconsin-Madison

    Runtime LOD - University of Wisconsin-Madison

    Times New Roman Symbol Default Design Microsoft Excel Worksheet Today Selecting LOD Models LOD Switching Fixed Cost Rendering Approximate Bin Packing Funkhouser and Sequin 93 Fix Cost Example Limitations of Static Models Progressive Meshes (PMs) PM Details Improving PMs Structure...
  • Healthy Eating: bringing the Eatwell Guide to life

    Healthy Eating: bringing the Eatwell Guide to life

    The blank Eatwell Guide. Blank Eatwell Guide. The Eatwell Guide. The UK's healthy eating model. Shows the proportions in which different food groups are needed in order to have a well-balanced and healthy diet. We should choose a variety of...
  • Will I be sued? Legal considerations in first

    Will I be sued? Legal considerations in first

    "The common law. generally does not impose a duty upon a person to take affirmative action to protect another from harm… The law draws a distinction between the creation of, or the material increase of, a risk of harm to...
  • staff.washington.edu

    staff.washington.edu

    ‏ Departmental (e.g. gCal, Blackboard, EC2/S3)‏ Central Policy/guideline framework Alumni Email via MS ExchangeLabs Google Apps discussion Broader MS apps discussion Investigating hosted exchange/sharepoint options Goal: enable individual & dept'l use with better compliance Things We Need from the Cloud...
  • presentazione_inglese_CaFoscari - Unive

    presentazione_inglese_CaFoscari - Unive

    Ca' Foscari University of Venice Dorsoduro 3246, 30123 Venice, Italy Website: www.unive.it Tel.: +39 041 234 8111 The city of Venice Venice stretches across 120 small islands in the Venetian Lagoon along the Adriatic Sea, in Northeast Italy.
  • Block Diagrams - UToledo Engineering

    Block Diagrams - UToledo Engineering

    Analogous Physical Systems BIOE 4200 Creating Mathematical Models Break down system into individual components or processes Need to model process outputs as function of inputs and process states Models can be obtained experimentally Measure output using a range of inputs...