Claims and Critical Thinking



Claims are either true or false.
Some sentences (even some nondeclarative
ones) make claims, but some (even some
declarative ones) do other things.
CRITICAL THINKING: The careful,
deliberate determination of whether to
accept, reject, or suspend judgment about a
claim – and the degree of confidence with
which we accept or reject it.
Skills Beneficial to Everyone


Critical thinking is not about attacking
and defeating others; it’s about helping
them and you.
Critical thinking is more a set of skills
than a set of facts.
Skills Involved in Critical
Thinking






Careful listening and reasoning
Finding hidden assumptions
Tracing the consequences of claims
Determining the credibility of sources
Recognizing and avoiding various sorts
of rhetoric and pseudoreasoning
Analyzing and evaluating arguments
Issues





Issue: a matter of controversy or uncertainty.
Issues may be internal (between self and
self) or external (between self and others.)
Topics of conversation aren’t issues unless
there is controversy or uncertainty that the
parties are trying to resolve.
Critical thinking requires identifying the issue,
separating it from others and focusing on it.
Issues should be kept straight and dealt with
in proper order (efficiently.)
Arguments



Arguments are one of the ways used to
settle issues.
Arguments attempt to support a claim
(the conclusion) by giving reasons for
believing it (the premises.)
The issue is “Whether or not the
conclusion is acceptable given the
premises.”
Facts and Opinions



“Fact” indicates that a claim is true.
“Opinion” indicates that a claim is
believed.
Clearly, some opinions are factual and
some aren’t.
Objectivity and Subjectivity





An issue is factual or (objective) if there are accepted
means for settling it.
An issue is a matter of pure opinion (subjective) if
both sides could be correct.
Objective claims are true or false regardless of our
inner states while subjective claims are usually just
expressions of inner states.
Controversy alone does not make an issue subjective;
equality of persons doesn’t mean equality of
opinions.
Disputes may arise over whether certain types of
claims (e.g., moral ones) are objective or subjective.
Organizing an Argumentative
Essay




Make the focus clear at the beginning.
Stick to the issue.
Arrange the elements in a logical order.
Be complete (easier with limited topics.)
Good Writing Habits





Outline after the first draft.
Revise, revise, revise!
Let others read and criticize.
Read it out loud.
When satisfied, put it aside for a while
then revise again!
Essays to Avoid





Windy preamble essays.
Rambling stream-of-consciousness
essays.
Knee-jerk reaction essays.
Glancing-blow essays.
Let-the-reader-do-the-work essays.
Clarity I: Definitions




A definition can serve different purposes: to
stipulate, to explain, to precise and to
persuade.
Definitions can be by example, by synonym
or analytical (genus-species).
Abstract terms may not be completely
definable.
The literal meaning of a term is distinct from
its emotive force (the denotation is distinct
from the connotation.)
Clarity II: Ambiguity




Ambiguous claims have more than one
meaning in the context.
In semantical ambiguity, specific words or
phrases have multiple meanings.
In syntactical ambiguity, the entire structure
of the sentence is at fault.
In grouping ambiguity, it isn’t clear whether
we are talking about the members of a group
collectively or individually.
Composition and Division
Fallacies


A composition fallacy occurs when we
argue that what holds true individually
must hold true collectively.
A division fallacy occurs when we argue
that what holds true collectively must
hold true individually.
Clarity III: Vagueness



A claim is vague if it doesn’t have a
precise enough meaning in the context.
Vagueness is a matter of degree.
Fuzzy words (“old”, “bald”, “rich”) can
produce vagueness, but you can be
vague even without them.
Clarity IV: Comparative Claims






Is important information missing?
Is the standard of comparison clear?
Is the same standard being used?
Are the same reporting and recording
practices being used?
Are the items really comparable?
Is the comparison an average and, if so,
what kind (mean, median or mode)?
When Should We Accept an
Unsupported Claim?


If it does not conflict with our
observations, our background
knowledge or other credible claims.
If it comes from a credible, unbiased
source.
Even Personal Observation
Can Be Unreliable



If the observing conditions are bad
If the observer is distracted or impaired
If the instruments used are faulty
Other Factors Affecting
Personal Observation





Individual powers of observation
Training and experience
Beliefs, hopes, fears, expectations, bias
Memory
Even so, personal observation is usually
the best source of information we have;
we should accept it unless we have a
specific reason to challenge it.
Does the Claim Conflict with
Background Knowledge




The less conflict, the higher the initial
plausibility of the claim.
If there is conflict we can rightfully reject the
claim even without evidence from personal
observation.
Remember: Some of your background beliefs
are surely wrong but you don’t know which.
The broader your background knowledge the
better!
Assessing the Credibility of a
Source: Expertise






Education
Experience
Accomplishments
Reputation (especially among other
experts)
Position
All, of course, in fields relevant to the
issue.
Why Experts Can Be Wrong



Expertise can be bought
The experts may disagree
The subject may be such that none can
claim expertise.
News Media





Print provides broader coverage than
electronic
Newspapers may feel pressure from
advertisers and the local public
Headlines are sometimes misleading
Size and location of the story may be
disproportionate to its importance
Opinion sometimes gets blended with facts
Slanters


These are the various linguistic devices
commonly used to attempt to persuade
without argument.
They rely on


the emotive force of words and phrases
and/or
linguistic manipulations that suggest
hidden meanings
Words of Caution on Slanters



Slanters are only bad when they are
used to mislead.
Slanters can be combined with perfectly
good reasoning (so don’t throw the
baby out with the bath.)
Sometimes its wise and good to slant.
Slanters I: Emotive Force



Euphemisms and Dysphemisms: Its all
in how you describe it…
Persuasive comparisons, definitions and
explanations: …or how you compare,
define or explain it.
Stereotypes: Just read the label.
Slanters II: Linguistic
Manipulation






Innuendo: “I never said he was drunk…”
Loaded questions: These have unjustified
hidden assumptions (innuendo in
interrogative dress)
Weaslers: Watering down a claim
Downplayers: The verbal brush-off
Hyperbole: Extravagant overstatement
Proof Surrogates: “Evidence” that isn’t
Manipulating the Information
I: The News




Most stories are given, not dug up; sources
must not be offended.
Since the news media are private businesses,
they mustn’t offend either advertisers or
audiences.
The result is bias, oversimplification, passivity
and an overindulgence in entertainment.
We will get the news we want and pay for.
Advertising



General Question: Does this ad give me a
good reason to buy the product?”
General Answer: Only if it establishes that I
will be better off with the product than
without it (or than with the money it will
cost).
Keep in mind:


Wants should be distinct from needs
Ads purposefully try to instill desires and fears we
previously lacked
Three Ways Ads Lacking
Reasons Can Persuade

By associating the product with pleasurable

By associating the product with people we

By associating the product with desirable

feelings
admire or wish to be like
situations
Unless availability is all you need to know,
buying a product based on a reasonless ad is
never justified.
What About “Promise Ads”
that Supply Reasons?




Claims in ads often come with no guarantees
and are notoriously vague, ambiguous,
misleading, exaggerated and wrong.
We only get the information the seller wants
us to have!
Our suspicions about ads in general justifies
suspicions about particular ads.
So even ads with reasons don’t in themselves
justify a purchase.
What Pseudoreasoning Is


No grounds for accepting a claim are
given even though something
approximating an argument may be
there.
Emotional appeals, factual irrelevancies
and persuasive devices are used to
induce acceptance of a claim.
Types of Psedoreasoning







Smokescreen/Red Herring
Subjectivist Fallacy
Common Belief
Common Practice
Peer Pressure/Bandwagon
Wishful Thinking
Scare Tactics
More Pseudoreasoning





Appeal to Pity
Apple Polishing
Horse Laugh/Ridicule/Sarcasm
Appeal to Anger or Indignation
Two Wrongs Make a Right
Even More Pseudoreasoning

Ad Hominem






Personal Attack
Circumstantial ad Hominem
Pseudorefutation
Poisoning the Well
Genetic Fallacy
Burden of Proof
Some Oldies but Goodies


Straw Man
False Dilemma




Perfectionist Fallacy
Line-Drawing Fallacy
Slippery Slope
Begging the Question
Arguments and Explanations


We give an argument to try to settle
whether some claim is true.
We give an explanation to try to
explain why some claim is true.
Argument or Explanation?





Sometimes the writer doesn’t know
They use the same words and phrases
(“reason”, “that’s why”, etc.)
The word “explanation” and its
derivatives can appear in arguments.
Explanations can be used in arguments.
Sometimes it depends on the context
and the interests of those concerned.
Explanations and Justifications



A justification is an argument in defense of an
action.
Although justifications often include
explanations, explanations can also be neutral
regarding approval or disapproval.
So not every attempt to explain something is
an attempt to justify it; explanations need not
imply approval.
Kinds of Explanations



Physical
Behavioral
Functional
Physical Explanations


These seek the physical background
causing the event in question.
The physical background consists of


The general physical conditions (usually
unstated)
That link of the causal chain leading to the
event which, based on our interests and
knowledge, we take as the direct or
immediate cause of the event.
Three Mistakes in Physical
Explanations



Tracing causal chains back too far
Expecting reasons and motives behind
all causal chains.
Giving physical explanations at the
wrong technical level for the situation
and/or audience.
Behavioral Explanations I



These attempt to explain behavior in terms of
psychology, political science, sociology,
history, economics or “common-sense
psychology”.
The causal background is historical.
Which factors (political, economic, social,
psychological) are important depends on our
interests and knowledge; there is no single
correct explanation of any voluntary behavior.
Behavioral Explanations II





Recurring patterns of behavior require theoretical
explanations.
Expect more exceptions to generalizations about
behavioral regularities than to generalization about
regularities in nature.
These explanations can also be traced inappropriately
far and pitched at the wrong technical level for the
audience.
Explanations by reasons and motives look forward,
unlike physical explanations.
Don’t confuse a reason (argument) for the reason
(explanation).
Functional Explanations




A functional explanation by puts a thing in a
wider context and then indicates the role it
plays in that context.
Actual and intended functions can differ.
An item may have more than one function.
Since functions usually depend on reasons
and motives, functional explanations are
often behavioral explanations “in passive
voice.”
Spotting Weak Explanations I





Testability: Beware of rubber “ad hoc”
explanations!
Noncircularity: Some explanations just
describe the phenomena in different words.
Relevance: Does the explanation allow us to
make predictions?
Not Too Vague: “He’s rude because he’s out
of sorts.”
Reliability: Does it lead to false predictions?
Spotting Weak Explanations II




Explanatory Power: The more it
explains the better (especially if it’s a
theory!)
Freedom from Unnecessary
Assumptions: The fewer the better.
Consistency with Well-Established
Theory
Absence of Alternative Explanations
Explanatory Comparisons



Analogies aren’t so much true or false as
either enlightening or unhelpful.
The best comparisons give us the greatest
number of close resemblances and the
shortest list of important differences.
The hearer must be familiar with both terms
of the comparison to understand and
evaluate it.
Argument=
Conclusion+Premises



A claim can be the conclusion of one
argument a premise in another.
An argument may have an unstated
premise or conclusion.
Premises can support the conclusion
dependently or independently.
Argument Terminology




A good argument gives grounds for accepting
the conclusion; a better argument gives more
grounds.
A valid argument is one which, if we assume
the premises to be true, the conclusion
cannot be false.
A sound argument is a valid argument with
true premises.
A strong argument is one which, if we
assume the premises to be true, it is unlikely
that the conclusion will be false.
Deduction and Induction


Deductive arguments are valid or
intended by their authors to be valid.
Inductive arguments are neither valid
nor intended by their authors to be
valid.
Unstated Premises



Is there a plausible claim that will make
the argument valid?
Is there a plausible claim that will make
the argument strong?
Be charitable in reconstructing the
arguments of others.
Evaluating Arguments


Do the premises support the
conclusion?
Are the premises reasonable?
Deductive Logic


Categorical (class) logic
Truth-functional logic
Standard Form
Categorical Claims





A: All __ are __. (quantity: universal, quality:
affirmative)
E: No __ are __. (quantity: universal,
quality: negative)
I: Some __ are __. (quantity: particular,
quality: affirmative)
O: Some __ are not __. (quantity: particular,
quality: negative)
The terms in the blanks must be nouns or
noun phrases (no bare adjectives)
The Square of Opposition


A and O propositions are contradictories, as
are E and I propositions (they never have the
same truth-value).
Assuming at least one member of the subject
class, A and E propositions are contraries
(they can’t both be true) and I and O
propositions are subcontraries (they can’t
both be false).
Categorical Operations



Conversion: Switch the subject and predicate
terms (valid for E and I but not for A and O)
Obversion: Change the quality (e.g.,
affirmative to negative) and replace the
predicate term with its complimentary term
(e.g., dogs to nondogs)
Contraposition: Convert then replace both
terms with their complimentary terms.
Categorical Syllogisms




A two-premise deductive argument in which
every claim is a standard form categorical
claim (A, E, I, or O).
Major term (P): The term that appears as the
predicate term of the conclusion.
Minor term (S): The term that occurs as the
subject term of the conclusion.
Middle term (M): The term that appears only
in the premises.
Testing for Validity with
Venn Diagrams




Overlapping circles, minor term on left, major
term on right, middle term lower middle.
Shade out a section to show there is nothing
there and put an X in a section to indicate
there is something there.
Always shade before Xing; diagram A and E
premises before I and O premises.
The argument is valid if, after diagramming
the premises you have already diagramed the
conclusion.
Distribution Patterns





A claim is said to distribute a term if it
says something about every member of
the class denoted by the term.
A: Subject term only distributed.
E: Both terms distributed.
I: Neither term distributed.
O: Predicate term only distributed.
The Rules Method of Testing
for Validity



The number of negative claims in the
premises must be the same as the number of
negative claims in the conclusion (=1).
At least one claim must distribute the middle
term.
Any term that is distributed in the conclusion
must also be distributed in the premises.
Inductive Arguments



Inductive arguments attempt to establish the
likelihood (not certainty) of their conclusions;
none are deductively valid.
They fall on a scale from very strong to very
weak depending on the degree of support
provided to the conclusion by the premises.
A basic inductive idea is that the more ways
some things are alike, the more likely it is
that they will be alike in some further way.
The Basic Argument Pattern


Analogical arguments compare
individuals; inductive generalizations
compare classes.
In both cases the basic argument
pattern is:




Premise: X has properties a, b, and c.
Premise: Y has properties a, b, and c
Premise: X has further property p.
Conclusion: Y also has further property p
Terminology



The “property in question” = the property
ascribed in the conclusion
The “sample” = the group whose members
we know have or don’t have the property in
question.
The “target population” = the individual or
group about which we are seeking to know
whether or not they have the property in
question (this may be a subset of the
sample.)
Analogical Arguments


The premises claim that one or more
items have a property, and the
conclusion claims that some similar item
has that property.
Example: The two Yugos I’ve previously
driven were underpowered so this one
will probably be underpowered too.
Factors Governing the Strength of
Analogical Arguments I



As long as all the members of the sample
have the property in question, the larger the
sample the stronger the argument.
The greater the percentage of the sample
that has the property in question the stronger
the argument.
The greater the number of the similarities
(and the smaller the number of
dissimilarities) between the target and the
sample the stronger the argument.
Factors Governing the Strength of
Analogical Arguments II


With regard to a feature that, to our
knowledge, the target may or may not
have, the more diverse the sample the
stronger the argument.
The less narrow the conclusion the
stronger the argument.
Inductive Generalizations




These always have a class as the target.
The sample is always drawn from the target
population.
Basic idea: If a part of a class has the
property in question then the class as a whole
probably has that property too.
The conclusion may refer to all, most, many,
or some specified percentage of the target
population.
Representativeness and Bias





A sample is representative of a target population if it
has all the relevant features of the target in the same
proportions.
A generalization not based on a representative
sample is untrustworthy.
An unrepresentative sample is called a biased
sample.
We can approximate a representative sample with a
random sample, one in which each member of the
target has the same chance of being in the sample.
Random sampling error can occur even with an
unbiased sample.
Sample Size, Error Margin,
and Confidence Level



The error margin is the range within which
the conclusion can be expected to fall.
The confidence level indicates the percentage
of random samples in which the property in
question falls within the error margin.
As the sample size increases, the error
margin will decrease or the confidence level
will increase or both.
Criteria and Fallacies of
Inductive Generalizations



The sample must be large enough to be
representative of the target population (if
not, Fallacy of Hasty Generalization.)
The sample must be unbiased with regard to
features relevant to the property in question
(if not, Fallacy of Biased Generalization.)
Basing a conclusion on a few clearly
unrepresentative cases is an extreme form of
hasty generalization called the Fallacy of
Anecdotal Evidence.
Untrustworthy Polls






Self-selected samples.
Person-on-the-street interviews.
Telephone surveys.
Questionnaires
Polls commissioned by advocacy
groups.
Push-polling.
The Law of Large Numbers



The larger the number of chance-determined
repetitious events considered, the closer the
alternatives will approach predictable ratios.
This is why we need a minimum sample size
even if it is random and unbiased.
Using the past performance of an event with
a predictable ratio to determine the odds that
the next occurrence will be a certain way is
the Gambler’s Fallacy.
Causation Among
Specific Events


X is the difference: X caused Y because
X is the only relevant difference
between the situation where Y occurs
and situations in which Y didn’t occur.
X is the common thread: X caused Y
because X is the only common factor in
multiple occurrences of Y.
Questions You Should Ask


X is the difference: Is X the only
relevant difference?
X is the common thread:


Is X the only relevant common factor?
Could Y have resulted from two
independent causes?
Four Common Types of Weak
Causal Arguments




Post Hoc, Ergo Propter Hoc: Y (or Y-type
events) is caused by X (or X-type event)
simply because Y came after X.
Ignoring a Possible Common Cause: X and Y
may both be caused by a third factor W.
Assuming a Common Cause: X and Y may be
coincidental, not causally related.
Reversing Causality: Y may be the cause of X
rather than X being the cause of Y.
Causation in Populations: The
Search For Causal Factors



Controlled Cause-to-Effect Experiments
Nonexperimental Cause-to-Effect
Studies
Nonexperimental Effect-to-Cause
Studies
Controlled Cause-to-Effect
Experiments



Randomly divide a random sample of the
target group into an experimental group to be
exposed to C and a control group treated the
same except not exposed to C.
Measure the difference (d) in frequency of E
between the two groups.
If d is sufficiently large, C is a causal factor
for E in the population (the target group.)
Considerations





Is d large enough for the given sample size to
be statistically significant?
Is the result being analogically extended to a
group other than the target population?
Is the sample from which the experimental
and control groups are taken representative
of the population (was it randomly selected?)
Are the experimental and control groups
selected randomly from this sample?
Is the study done by a reputable, unbiased
organization?
Nonexperimental Cause-toEffect Studies



The same as experimental cause-to-effect studies
except that the “experimental” group is not exposed
to C by the investigators.
Important difference: Even if we choose the
experimental and control groups randomly from our
sample, they may not be alike in all respects relevant
to C since exposure to C in the general population
may be linked with other factors that make them
relevantly different from the control group.
We can address this problem by nonrandomly
selecting the control group to match the experimental
group if factors that might be relevant to E.
Nonexperimental Effect-toCause Studies



Compare an experimental group already displaying E with a control group none of whom
display E in terms of the frequency of a
suspected cause C.
If the frequency of C in the experimental
group significantly exceeds its frequency in
the the control up, C may be said to cause E
in the target population.
Do the subjects in the experimental group
differ in some important way from the rest of
the population? If so, these factors need to
be controlled.