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ENGC44H3 (2)
Ted Petit (1)
Chapter 8-11

HUMA02H3 at UTSC Chapter 8-11.doc

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University of Toronto Scarborough
Ted Petit

STUDY NOTES ** ( THEY ARE ON THE FINAL) Final exam questions from midterm: o Usually the burden of proof rests on the side that  Makes a positive claim o In good arguments, premises are always explicit  False o There is something inherently wrong with accepting a claim that furthers your own interests  False o Knowledge requires certainty  False o Doubt is always reasonable  False Chapter 8: Inductive Reasoning - 1. Enumerative Induction: a way of reasoning where we begin with observations about some members of the group and end with a generalization about all of them o Particular  General o Natural and useful o Formal form:  X % of the observed members of Group A have property P. Therefore, X % of all members of group A probably have property P. o Target population/Target group: the whole collection of individuals in question o Sample members/Sample: the observed members of the target group o Relevant property/Property in question: the property we’re interested in o The strength of the argument depends on the premises as well as on how much is claimed in the conclusion o Enumerative Inductive arguments fail to be strong when sample is: 1. TOO SMALL  Smaller sample = limit generalization of the target group 2. NOT REPRESENTATIVE  Sample must be representative of the whole o Sample Size:  Hasty Generalization: draw conclusions about a target group on the basis of a too small sample • E.g. polls, consumer opinion surveys, scientific studies (medical research), QC checks, anecdotal reports etc.  Larger sample = more likely it is reliably reflecting the nature of the larger group  **The more homogeneous(identical/uniform) a target group is in traits relevant to the property in question, the smaller the sample can be; the less homogenous, the larger the sample should be o Representativeness:  Representative sample: it must resemble the target group in all the ways that matter  Does not represent the target group = biased sample  The sample must be like the target group by: • Having all the same relevant characteristics (that influence the property in question) • Having them in the same proportions that the target group does (e.g. 50% catholic = sample 50% catholic)  Selective Attention: the tendency to observe and remember things that reinforce our beliefs and to gloss over the dismiss things that undercut those beliefs  Reason for bias sampling o Common problems in poll questions:  Phrasing of Question: wording of the questions is skewed (e.g. choice of wording more negative)  Order of Questions: the first question can affect the respondent’s perspective on second question  Restricted Choices: False Dilemma/condense broad spectrums of opinions on issues into convenient choices o Opinion polls are used to arrive at generalizations (enumerative inductions reach a high level of sophistication in this form)  Be strong 1 STUDY NOTES  Have true premises 1. Use a sample that is large enough to represent the target population accurately in all the relevant population features 2. Generate accurate data (results must correctly reflect what they purport to be about) o FAILED POLLS = data-processing errors, botched poll interviews, poorly phrased questions, etc. o National polling: samples need not be enormous to be accurate reflections of the larger target population o Random Sampling: to ensure that a sample is truly representative of the target group, sample must be selected randomly from the target group  Simple Random Selection: every member of the target group has an equal chance of being selected for the sample • Assign a number to each member of the population then use a random-number generator to make the selections (avoid biases made consciously/unconsciously by humans) o Self-selecting Samples: allowing survey subjects to choose themselves (TELLS YOU VERY LITTLE ABOUT THE TARGET POPULATIONS) o Biased o Every instance of sampling is only an approximation of the total population results  Margin of Error: each attempt at sampling will yield slightly different results • E.g. + 3% or -3% of %points from 62%  Confidence Level: the probability that the sample will accurately represent the target group within the margin of error • confidence level: 95% (usual value) chance that the results from sample (taking into account the margin of error) will accurately reflect the result that we would get if we polled the entire target population • only refers to sampling error: the probability that the sample does not accurately reflect the true values in the target population o SS: 600, MoE: 4%...SS:1000, MoE 3% ** enlarging the sample substantially (beyond 1000) does not substantially decrease the margin of error o Lower confidence level (desired) = smaller sample size o Larger the margin of error = the higher the confidence level can be ( high chance of falling within the MoE range) - A. Statistical Syllogism: inductive arguments that apply a statistical generalization –a claim about what is true of most members of a group or category –to a specific member of that group or category o Formal Form:  Premise 1: A proportion X of the group M have characteristic P.  Premise 2: Individual S is a member of Group M.  Conclusion: Individual S has characteristic P. o Must identify: (IGCP or C-PIG)  The individual being examined  The group to which the individual is said to belong  The characteristics being attributed  The proportion of the group said to have that characteristic (% or Fraction…or probability words) o General  Particular (Premise 1 of Statistical Syllogism = Conclusion of Enumerative Induction) o Evaluating Statistical Syllogisms: 1. Acceptable Premises 2 STUDY NOTES • Premises = Common knowledge? Careful survey (Randomly Selected Sample)? • Grounding of the generalization is weak = argument is weak 2. Statistical Strength • Strength of generalizations being offered • 60% vs. 99%...vague words like “a lot” or ”most”= 51%? 3. Typical or Randomly Selected • Consider whether the individual person or item under consideration is likely to be a typical member of the group, or whether you have reason to believe he/she/it is an exception to the rule • E.g. 85% of Canadians don’t know CPR…Talk to a man with doctor’s uniform (not Randomly selected…and not typical) - 3. Analogical Induction (argument by analogy) o Analogy: a comparison of two or more things alike in specific respects (often in forms of similes) o Because two or more things are similar in several respects, they must be similar in some further respect o Formal Form:  Thing A has properties P 1 P 2 and P 3lus the property P .4 Thing B has properties P 1 P 2 and P 3 Therefore, thing B probably has the property P . 4 • Humans can move about, solve mathematical problems, win chess games, and feel pain. Robots are like humans in that they can move about, solve mathematical problems and win chess games. Therefore, it’s probable that robots can also feel pain. o The greater the degree of similarity between the two things being compared, the more probable the conclusion is o ** enumerative induction argues from some members of a group to the group as a whole  Argues from the properties of a sample to the properties of the whole group o ** analogical induction reasons from some (1 or more) individuals to one further individual  Reasons from the properties of one or more individuals to the properties of another individual o Used in law, science, medicine, ethics, archaeology and forensics  E.g. mice vs. human + drugs o Judging the strength of arguments by analogy: 1. Relevant similarities • More relevant similarities between the things being compared = more probable conclusion • Inference can be strong, even if it is not cogent (premises not true) • Similarities cited as part of an analogical argument clearly has to be connected in some significant way to the conclusion being argued for o Has effect on whether the conclusion is probably true o Explanation may be required to show WHY a particular similarity is actually relevant (burden of proof) 2. Relevant dissimilarities • More relevant dissimilarities (disanalogies) between the things being compared = the less probable the conclusion • Weakens/undermines arguments by analogy 3 STUDY NOTES 3. The number of instances compared • Greater the # of instances/cases that show the relevant similarities = the stronger the argument o E.g. 5 wars instead of 1 (Vietnam war vs. current war) 4. Diversity among cases • The greater the diversity among the cases that exhibit the relevant similarities = the stronger the argument • Suggest that the similarities are not accidental or contrived  linked even in a variety of situations o E.g. young, middle-aged, old professors + all with same similarities = good course - 4. Causal Arguments o Causal claim: a statement about the causes of things o Causal argument: an inductive argument whose conclusion contains a causal claim o Sometimes reason about cause and effect by using enumerative induction, analogical induction o Can be enumerative inductions, analogical inductions, or rely on Mill’s Method o Inference to the best explanation: reason from premises about a state of affairs to an explanation for that state of affairs  Reason to a causal conclusion by pinpointing the best explanation for a particular effect  Powerful and versatile form of inductive reasoning  Essence of scientific thinking o Mill’s method’s of inductive inference (ways of evaluating causal arguments): 1. Agreement or Difference • Method of Agreement: If two or more occurrences of a phenomenon have only one relevant factor in common, that factor must be the cause o E.g. factor c consistently accompanies effect E in all instances :. c brings about E o Pg. 303 • Method of Difference: the relevant factor that is present when a phenomenon occurs and that is absent when the phenomenon does not occur must be the cause o Factors that are points of differences among the instances o E.g. factor c is probably the cause of E because all the other factors are the same 2. Both Agreement and Difference • Joint Method of Agreement and Difference: the likely cause is the one isolated when you: o Identify the relevant factors common to occurrences of the phenomenon (Method of Agreement) o Discard any of these that are present even when there are no occurrences (Method of Difference) • Increases the probability that the conclusion is true • Modern “controlled trials” used to test the effectiveness of medical treatments o 2 groups + controlled vs. experimental + IV vs DV 3. Correlation • Relevant factors aren’t merely present or absent during occurrences of the phenomenon – they are closely CORRELATED with the occurrences • Cause of occurrence varies as the occurrence (effect) does 4 STUDY NOTES • Method of Concomitant Variation: when two events are correlated – when one varies in close connection with the other – they are probably causally related • Indirect evidence of one thing causing another  decrease + increase factor o Dose-response relationship: the higher the dose of the element in question, the higher the response • ** correlations can be just COINCIDENCES o Causal Confusions: 1. Misidentifying Relevant Factors • Are the factors preceding the effect truly relevant to that effect? • Depends on background knowledge o Lack of it can lead you to dismiss or ignore relevant factors or to assume that irrelevant factors must play a role o Cure for this inadequacy: deeper study of the causal possibilities in question 2. Mishandling Multiple Factors • Too many relevant factors to consider (cannot narrow possibilities to just 1) • Causal reasoning is often flawed because of the failure to consider ALL the relevant antecedent factors (failure to consider alternative explanations) o Skimpy background knowledge 3. Being Misled by Coincidence • Interesting correlations of events that are actually just coincidences • Want a coincidence to be a cause-and-effect relationship • Incredible coincidences are common and must occur o Any event, even one that seems shockingly improbable, is actually very probable over the long haul • Misjudging probabilities involved (remembering only what we want) • ** done assume that a causal connection exists unless you have good reason for doing so (research, or evaluation) o Confusing Cause with Temporal Order  Post hoc, ergo propter hoc (after that, therefore because of that): a particularly common type of misjudgement about coincidences  Other considerations besides temporal order would have to apply 1. Ignoring Common-Causal Factor • A and B are caused by some third factor C  A doesn’t cause B and vice versa • Ice cream & deaths due to drowning (both more common during a particular season) o Common causal factor shared by those two variables is: SUMMER 2. Confusing Cause and Effect • Sometimes we may realise that there’s a causal relationship between two factors BUT we may not know which factor is the cause and which is the effect • ** done assume that a causal connection exists unless you have good reason for doing so (research, or evaluation) o Necessary and Sufficient Conditions  Causal processes always occur under specific conditions 5 STUDY NOTES • The conditions for the occurrence of an event  Necessary condition for the occurrence of an event: is one without which the event cannot occur (X is needed for Y to happen)  Sufficient condition for the occurrence of an event: is one that guarantees that the event occurs ( X is enough to…)  Dropping a water balloon: • NC: releasing balloon, gravity, breakable material of balloon, hard pavement • SC: all four conditions of NC are guarantees that it will cause the balloon to break  Set of necessary conditions constitutes a sufficient condition for an event: individually necessary and jointly sufficient  Set of conditions individually necessary but NOT jointly sufficient • E.g. some conditions are necessary for sustaining the goldfish’s life are present, but not all of them are. Because the necessary conditions are missing, the sufficient condition keeping the fish alive would not exist.  Set of conditions that are jointly sufficient but not individually necessary • E.g. by not feeding the fish for weeks, we would create a set of conditions sufficient for the death of the fish, but these conditions are not necessary for the death of the goldfish (there are other ways)  Necessary causal conditions: interested in preventing or eliminating a state of affairs  Sufficient causal conditions: when we are interested in BRINGING about a state of affairs • “if” • Sure to happen if the condition obtains  Necessary condition • “only if” • This may not be the only necessary condition:. May or may not occur even if the condition obtains  Necessary and sufficient condition: • “If and only if” - Mixed Arguments: arguers will often combine inductive and deductive elements within a single, compound argument. o No limits of combinations o Premises of the categorical syllogism is actually the conclusion of an inductive argument  No S are M All P are M Therefore, no S are P o Mixed argument as a whole is weak (?)
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