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# FSU PHI 2100 - Exam 2 Review Packet

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RCT Exam 2 Review Packet Deductive vs. Inductiveo Deductive is one that is claimed or intended to be valido Inductive are not given with the intention of being valid. o Deductive Standard= validityo InductiveStandard= Strong reasonso Adding premises to a valid argument cannot make it invalid, but adding to an inductive argument can make it worse.o Valid deductive arg are indefeasible, as no new info can make them invalido Strong inductive arg are defeasible as new info can undercut the strength of the argument and the support that the premises give for the conclusion. o Deductive = logical evaluationo Inductive= support based Statistical Generalizations : Occur when one uses a sample portion of a population and then draws a conclusion about the population as a whole. o Sample of the population was surveyed and results are extrapolated and whatever was true from it, is said to be true of the whole. o You can tell when a sample size isn’t large enough. If its not, it’s a hasty argument generalization-fallacy o Important to determine if the sample is biased If you test at only 1 location  Fallacy of Biased Sampling o Ask: Are the premises acceptable? Is the sample too small? Is the sample biased? Are results affected by other sources of bias?  Statistical Application: Opposite of Generalization. From information concerning a population, we draw a conclusion concerning a member or subset of that population. o 97% of Repub in cali voted for McCain. Marvin is a republican from Cali. Therefore, marvin voted for McCaino When evaluation the strength of stat applications, the percentage of F’s that have the feature G is important. As the figure approaches 100%, the argument gains strength. Can also get strong Stat applications when the figure approaches 0%. o When the percentages are in the middle range, such stat applications are weak.  Reference Class : F of a Stat Application is the reference class. Ex: republican party in above example. o Should choose a reference class in a way that brings all relevant evidence to bear on the subject. o One way in determining which reference class is stronger is to combine them: What % of Republicans from California who were relatives of Obama voted for mccain. o To be successful, such reasoning must take place within a broader framework that helps determine which features are significant and which features are not.  Sufficient and Necessary Conditionso Feature F is a sufficient condition for feature G if an only if anything that has feature F also has feature G.o Feature F is a necessary condition for feature G if and only if anything thatlacks feature F also lacks feature G. o Ex: Mercury is a sufficient condition for being a metal, but its not necessary because there are other metals. The Sufficient Condition Test (SCT)o Any candidate that is present when G is absent is eliminated as a possible sufficient condition of G. Case1 A B C D GCase 2 ~A B C ~D ~GCase 3 A ~B ~C ~D ~G Bcz G is present in Case 1 it can be ignored.  Bcz B and C are present when G is absent so it cannot be sufficient D isn’t ruled out Ruling out a candidate is deductive and indefeasible  The Necessary Condition Test (NCT)o Any candidate that is absent when G is present is eliminated as a possible necessary condition of G.Case 1 A B C D ~GCase 2 ~A B C D GCase 3 A ~B C ~D G A,B,D ruled out bcz they are absent when G is present C isn’t ruled out as a candidate, but need more testing Rigorous Testing o Watch for odd in which candidate is always present, therefore and cannot fail NCT. o Beware of cases which candidate is always absentCase 1 A ~B C D GCase 2 A ~B ~C ~D ~GCase 3 A ~B C ~D ~GCase 4 A ~B ~C D G A fails SCT in Case 2 and 3 but passes NCT.  B Passes SCT but fails NCT in Case 1 and 4  C is eliminated by both Only D is not eliminated by either test, so it is the only candidate for being both a necessary and sufficient condition of G.o Besides looking for diversity in candidates, we should look for diversity intarget feature G. o To test rigorously, it involves seeking out cases in which failing the test is a live possibility.o SCT Cand=present and Target= Absento NCT Cand=absent and Target=Present Applying these Tests to Find Causeso Normality : our statements of sufficient and necessary conditions must be contextualized to normal situations. o To determine which cases are relevant, we take certain background assumptions for granted.o Sometimes beliefs are common or specialized knowledge. o Begin with a defeasible background assumption were looking for one cause that iscausing one illness.  then apply NCT and SCTo Difference between saying something is a casual factor as opposed to a cause.  Permanent features of the context as casual factors and of changes that occur before the effect as “the cause”  Doesn’t always hold: we would say the cause of contracting legionaires disease was the weakened immune sys of those who contracted it.o Whatever survives after NCT/SCT Testing may be called a casual condition or factor if they fit in well with our system of other casual generalizations.o Some of these casual conditions will be called causes if they play a key role in ourcasual investigations.  Concomitant Variationo The SCT/NCT require that sometimes some feature is present and sometimes is absento Sometimes a feature is always present, but just to a greater or less degreeo Can use Concomitant Variation to see whether some feature is significantly responsible for some other feature. o Ex: Acid Rain. Some Acid Rain is always present in the atmosphereo If A varies directly in proportion to B, so that when A increases B increases and When A Decreases, B Decreases.  A & B = Positive Correlationo If A varies directly in proportion to B, so that when A decreases, B increases  A& B = negatively correlated o Difficult to determine whether there is causation where there is just correlation. o When a correlation:  A is cause of B B is cause of A Some 3rd thing is the cause of both A & B  Correlation= accidentalo Have to be somewhat related NOT unrelated. Inference to the Best Explanationo Once we settle on a cause, we can argue that the cause has more explanatory value for some phenomenon than other causes. o If the hypothesis fits well with our framework of beliefs and allows us to explain something better than any other hypothesis then we have a

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