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FHHM 1012 Critical Thinking
Lecture 6
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TOPIC 5
(INDUCTIVE REASONING 1)
i) Inductionii) Analogy
iii) Numbers
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Lecture Notes 2008 McGraw HillHigher Education 3
Inductive Argument
Inductive Argument: an argument in whichthe premises are intended to provide support, butnot conclusive evidence, for the conclusion.
Strong Inductive Argument: an inductiveargument in which the premises actually do makethe conclusion more likely to be true (rather thanfalse).
Remember, strength comes in degrees.
Cogent Inductive Argument: a stronginductive argument with true premises.
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How can you know if the argument isinductive?
If the arguments conclusiondoes notfollow with strict logical necessity fromits premises, the argument shouldnormally be treated as inductive.
Indicator words: likely, probably, its plausible(possible/probable/believable/ reasonable) tosuppose that, etc.
Common patterns: Analogy, Statistical,Causal and Generalization
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INDUCTIVE ARGUMENT
An inductive argument is one inwhich the premises are supposed tosupport the conclusion in such away that if the premises are true, itis improbable that the conclusionwould be false. Thus, the conclusion
follows probably from the premisesand inferences.
Lecture Notes 2008 McGraw HillHigher Education 5
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INDUCTIVE ARGUMENT
Example of an inductive argument:
1. Socrates was Greek. (premise)
2. Most Greeks eat fish. (premise)3. Socrates probably ate fish.(conclusion)
Note: Premises do not provide
conclusive (convincing/ certain)evidence for the conclusion.
Lecture Notes 2008 McGraw HillHigher Education 6
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STRONG INDUCTIVEARGUMENT
A strong induction is an argumentin which the truth of the premiseswould make the conclusion probable
but not require it as being factual.
All observed crows are black.
Therefore: All crows are black.
Lecture Notes 2008 McGraw HillHigher Education 7
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STRONG INDUCTIVE
ARGUMENT
The sample of crows was unbiasedand large enough.
Thousands of randomly observedcrows over hundreds of years areblack.
We have good reason to accept the
conclusion as probably true, so theargument is strong.
Lecture Notes 2008 McGraw HillHigher Education 8
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Lecture Notes 2008 McGraw HillHigher Education 9
Argument by Analogy Comparison of things based on similarities.
Argument from analogy: an argument thatsuggests that the presence of certain
similarities is evidence for further similarities.
Common Form:1. A has characteristic X2. B has characteristic Y
3. A has characteristic Y4. So B probably has characteristic Y too.
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Argument by Analogy Example:
1. Tiffany and Heather are both tall andplay basketball.
2. Tiffany also plays volleyball.
3. So, Heather probably plays volleyballtoo.
Lecture Notes 2008 McGraw HillHigher Education 10
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Lecture Notes 2008 McGraw HillHigher Education 11
Evaluating Arguments from Analogy
Are the premises true?
Are the similarities relevant? Since being tall is helpful in volleyball,
the fact that both Tiffany and Heatherare tall is relevant to the previousconclusion.
The more relevant similaritiesthere are, the better. If we also learn that they both get
scholarships if they play more than onesport, our conclusion is more supported.
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Are there relevant dis-similarities?
Irrelevant dis-similarities: hair color
Relevant dis-similarity: job status
The more examples which are alsosimilar, the better.
If Amber and Krissy are also tall and play both
basketball and volleyball our conclusion is evenfurther supported.
Evaluating Arguments from Analogycont.
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The more diversity in theexamples, the better. If Tiffany, Amber and Krissy are different in many
ways, except for the fact that they are all tall andplay basketball and volleyball, it seems morelikely that their being tall and playing basketballis relevant to their playing volleyball. Thus,Heathers being tall and playing basketball isbetter evidence that she also plays volleyball.
Is the conclusion too specific?Heather probably plays volleyball is better
supported than Heather must play volleyball.
Evaluating Arguments from Analogy cont.
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Lecture Notes 2008 McGraw HillHigher Education 14
Arguing by Analogy
Employ the same questions andevaluation as you construct your ownarguments from analogy.
Dont be too specific.
Use relevant similarities.
Use many similarities.
Use a diverse and large group.
Use true premises.
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Pupils are more like oysters thansausages. The job of teaching is not tostuff them and then seal them up, butto help them open and reveal the richeswithin. There are pearls in each of us, ifonly we knew how to cultivate them
with ardor(love/passion/enthusiasm)and pers is tence(determinat ion) .
(Sydney J. Harris, "What True Education Should Do," 1964)
Exercise: Identify the Analogy
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Numbers
Numbers are a way of measuringand also important in reasoning.
Numbers can be misleading or usedwrongly.
A vague claim doesnt get anybetter by using numbers.
Lecture Notes 2008 McGraw HillHigher Education 16
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Misleading claims with numbers
Example (i): Zoe has 4 apples andZack has 2 oranges. Who has more?
More of what? A vague (unclear)or meaningless
comparison gets no better byhaving a few numbers in it.
Lecture Notes 2008 McGraw HillHigher Education 17
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Misleading claims with numbers
Example (ii) : There were twice asmany rapes as murders in our town
Lecture Notes 2008 McGraw HillHigher Education 18
Yes, thats a claim , but a misleading one.
It seems to say something important, butwhat?
Is this a claim?
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Misleading claims with numbers
Example (iii):
There were nearly 7.2 millionpeople around the globe in year
2000 who had at least $1 million ininvestable assets.
Lecture Notes 2008 McGraw HillHigher Education 19
What is the source of the figures?
What study?Who wants to let people know that theyre rich?
Therefore, this is a worthless report.
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Graphs
Useful in making comparisonsclearer.
Have to be careful wheninterpreting graphs because theycan conceal claims, mislead and justbe wrong.
Lecture Notes 2008 McGraw HillHigher Education 20
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Graphs
Lecture Notes 2008 McGraw HillHigher Education 21
Why do the graphs look different?
The vertical scales differ ( Misleading)
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Graphs
Explain why the graphs look different.
The graphs look different due to the differentvertical scales. Graph A appears to show a more
rapid decrease in sales than graph B.
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Averages
Example:
It ought to be safe to cross here. I
heard that the average depth is onlytwo feet.
Beware: The average is not themaximum or most likely depth.
Lecture Notes 2008 McGraw HillHigher Education 23
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Averages
The average or mean : collection ofnumbers is obtained by adding thenumbers and then dividing by thenumber of items.
Example: The average of1,2,3,4,5,5 ,8 is calculated:
Add: 1+2+3+4+5+5+8= 28
Divide: 28 by 7= 4 (The average)
Lecture Notes 2008 McGraw HillHigher Education 24
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Averages
Median : The midway mark, thesame number of items above andbelow.
Example median of 1,2,3,4,5,5,8 is 4
Mode : The number most oftenobtained.
Mode for 1,2,3,4,5,5,8 is 5
Lecture Notes 2008 McGraw HillHigher Education 25
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Averages
Unless the average is pretty close tothe median, the average does nottell you anything important.
Lecture Notes 2008 McGraw HillHigher Education 26
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Identifying Variables
o VariableAny observation that cantake different values.
o Examples:
o Raceo gender
o curriculum used
o student outcomes
o
math programo student attitude
o parent satisfaction
Lecture Notes 2008 McGraw HillHigher Education 27
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Two Kinds of Variables
Independent variable (IV) variable that is varied or manipulated by the researcher
Dependent variable (DV) the response that is measured
Choice of IV Change in the DV(actions/interventions) ( results/outcomes)
Lecture Notes 2008 McGraw HillHigher Education 28
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Some tips!
In the statement a researcher is measuring
the effect of chocolate on happiness
Start by identifying the dependent variable.
What is actually being measured in theexperiment?
DV: happiness
happiness is affected by chocolate, or
happiness depends on chocolate.
www.sci.sdsu.edu 29
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Identify the independent variable.
If a researcher is measuring the effect of
chocolate on happiness, the IV is chocolate.
This is the variable that the experimenter
will manipulate to see how it affects the
dependent variable (in this case
happiness).
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Example 1
"There will be a statistically significant difference in
graduation rates of at-risk high-school seniors who
participate in an intensive study program as opposed to
at-risk high-school seniors who do not participate in the
intensive study program."
(LaFountain & Bartos, 2002, p. 57)
IV :
DV :
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Example 2
32
A researcher is studying the effect of sleep on
aggression, thinking that less sleep will lead to more
aggression. She has some people sleep 6 hours per
night, some people sleep 3 hours per night and some
people sleep as much as they want. She thenmonitors aggressive behavior during basketball
games among participants.
IV :
DV :
http://www.sci.sdsu.edu
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Example 3
A researcher is curious to find out what effect classical
music has on peoples level of relaxation (as measured
by heart rate). He suspects that listening to classical
music will make people feel more calm and relaxed.
He lets one group listen to classical music for onehour. He lets another group sit in a quiet room for one
hour (i.e they hear no music). After one hour, he
monitors the heart rate of each participant to measure
their level of relaxation.
IV :
DV :
33http://www.sci.sdsu.edu
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Reference:
Epstein, RL. (2006). Critical Thinking. (3rd
ed.) Belmont: Wadsworth.(pg 253 275)
Basham, G., Irwin, W., Nardone, H. &
Wallace, JM. (2008). Critical Thinking: A
Students Introduction. (3rd ed.) New York:
McGraw-Hill.(pg 312 - 321)
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Overview of Todays Lecture
Common Patterns of InductiveReasoning
Induction
Argument from Analogy
Statistical Argument
Questions or Enquiries???
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The End