PHIL2420 Lecture Notes - Lecture 6: Inductive Reasoning, Statistical Inference, Binomial Distribution
18 May 2018
School
Department
Course
Professor
PHIL2420(
13(
6. Probability,(Sampling(and(Statistical(Inference(
(
10.$Inductive$Reasoning$
− An(inductively(strong(argument(is(one(that(is(an(invalid(argument(and(where(the(conclusion(is(highly(
likely(to(be(true(given(that(the(premises(are(true.((
10.1$Inductive$Strength$
− When(an(argument(is(inductively(strong,(we(are(saying(that(although(the(premises(of(the(argument(do(
not(logically(entail(the(conclusion,(the(premises(nonetheless(provide(strong(support(for(the(
conclusion(
− Inductive(strength(of(an(argument(is(a(measure(of(the(degree(of(support(that(is(provided(
− Inductive(strength(will(vary(from(0(to(an(upper(limit(of(1,(which(corresponds(to(deductive(validity(
10.2$Defeasibility$of$Inductive$Reasoning$
− Inductive(strength(is(defeasible(
− Adding(new(premises(to(a(valid(argument(will(not(make(it(invalid(
10.3$Cases$of$Inductive$Reasoning$
− Induction(based(on(statistics:(relying(on(statistics(to(make(generalizations(about(groups(of(things,(and(
to(make(predictions(about(particular(cases(
− Induction(based(on(analogy:(arguments(where(two(objects(A(and(B(are(very(similar,(and(so(we(
conclude(that(something(that(is(true(of(A(ought(to(be(true(of(B(as(well(
− Induction(based(on(inference(of(the(best(explanation:(sometimes(evidence(can(be(conflicting(and(
point(to(different(conclusions(
(
( (
