LOGIC-AND-CRITICAL-THINKING-MDH-PDFDrive-.pdf

LOGIC AND CRITICAL THINKING PPT

1. 1
CT3340/CD5540
Research Methodology for Computer Science and Engineering
Theory of Science
LOGIC AND CRITICAL THINKING
Gordana Dodig-Crnkovic
Department of Computer Science and Engineering
Mälardalen University
2004

2. 2
Theory of Science Lectures
1. SCIENCE, KNOWLEDGE, TRUTH
2. SCIENCE, RESEARCH, TECHNOLOGY AND SOCIETY
3. LOGIC AND CRITICAL THINKING 4. FORMAL LOGICAL SYSTEMS LIMITATIONS,
LANGUAGE AND COMMUNICATION,
BRIEF RETROSPECTIVE OF SCIENTIFIC THEORY
5. PROFESSIONAL ETHICS

3. 3
2
LOGIC AND CRITICAL THINKING
•LOGICAL ARGUMENT
•DEDUCTION
•INDUCTION
–Empirical Induction
–Mathematical Induction
–Induction vs. Deduction, Hypothetico-deductive Method
•REPETITIONS, PATTERNS, IDENTITY
•CAUSALITY AND DETERMINISM
•FALLACIES

4. 4
LOGIC
Logic is the science of reasoning, proof, thinking, or
inference. Logic allows us to analyze a piece of reasoning
and determine whether it is correct or not. To use the
technical terms, we determine whether the reasoning is
validor invalid.

5. 5
LOGIC
This lecture deals only with simple Boolean logic.
Other sorts of mathematical logic, such as fuzzy logic, obey
different rules.
When people talk of logical arguments, though, they
generally mean the type being described here.

6. 6
What is an Argument?
"An argument is a connected series of statements intended to
establish a proposition".
(Michael Palini Monty Python’s Argument Clinic.) See Monty Python's Argument Clinic under:
http://www.duke.edu/~pms5/humor/argument.html

7. 7
What is an Argument?
There are three stages to an argument:
– premises
– inference and
– conclusion.

8. 8
JUDGEMENT
Now, the question, What is a judgement? is no small question,
because the notion of judgementis just about the first of all
the notions of logic, the one that has to be explained before
all the others, before even the notions of propositionand
truth, for instance.
Per Martin-Löf On the Meanings of the Logical Constants and the Justifications of the Logical
Laws; Nordic Journal of Philosophical Logic, 1(1):11 60, 1996.

9. 9
What is an Argument?
An argument is thus a statement logically inferred from
premises. Neither an opinion nor a belief can qualify as an
argument!
Two sorts of arguments:
–deductive
–inductive

10. 10
What is an Argument?
The building blocks of a logical argument are propositions, also
called statements. A proposition is a statement which is
either true or false; for example:
"The first programmable computer was built in Cambridge."
"Dogs cannot see color."

11. 11
1. Premises
One or more propositions are necessary for the argument to
continue. They must be stated explicitly. They are called
the premises of the argument.
They are the evidence (or reas ons) for accepting the argument
and its conclusions.

12. 12
2. Inference
The premises of the argument are used to obtain further
propositions. This process is known as inference. In
inference, we start with one or more propositions which
have been accepted. We then derive a new proposition.
There are various forms of valid inference.
The propositions arrived at by inference may then be used in
further inference. Inference is often denoted by phrases
such as implies thator therefore.

13. 13
3. Conclusion
Finally, we arrive at the conclusion of the argument, another
proposition. The conclusion is often stated as the final
stage of inference. It is affirmed on the basis the original
premises, and the inference from them.
Conclusions are often indicated by phrases such as therefore,
it follows that , we concludeand so on.

14. 14
Deductive inferences:
general →particular
Inductive inferences:
particular →general

15. 15
DEDUCTION
A deductive argument is defined as:
• constructed according to valid rules of inference
• the conclusion necessarily follows from the premises.

16. 16
Modus Ponens
All humans are mortal. (premise)
Kevin is human. (premise)
Thus, Kevin is mortal. (conclusion)

17. 17
Modus Tollens
All birds have wings. (premise)
Kevin has no wings. (premise)
Kevin is not a bird. (conclusion)

18. 18
DisjunctiveSyllogism
The baby can either be a boy or a girl. (premise)
The baby is not a girl. (premise)
The baby is a boy. (conclusion)

19. 19
Hypothetical Syllogism
If Karro is a terrier, Karro is a dog. (premise)
If Karro is a dog, Karro is a mammal. (premise)
If Karro is a terrier, Karro is a mammal. (conclusion)

20. 20
NON-STANDARD LOGICS
•Categorical logic •Combinatory logic •Conditional logic •Constructive logic •Cumulative logic •Deontic logic •Dynamic logic •Epistemic logic •Erotetic logic •Free logic •Fuzzy logic •Higher-order logic •Infinitary logic •Intensional logic •Intuitionistic logic •Linear logic
•Many-sorted logic •Many-valued logic •Modal logic •Non-monotonic logic •Paraconsistent logic •Partial logic •Prohairetic logic •Quantum logic •Relevant logic •Stoic logic •Substance logic •Substructural logic •Temporal (tense) logic •Other logics

21. 21
NON-STANDARD LOGICS
http://www.earlham.edu/~peters/courses/logsys/nonstbib.htm http://www.math.vanderbilt.edu/~schectex/logics/

22. 22
INDUCTION
•Empirical Induction
•Mathematical Induction

23. 23
EMPIRICAL INDUCTION
The generic form of an inductive argument:
•Every A we have observed is a B.
•Therefore, every A is a B.

24. 24
An example of inductive inference
• Every instance of water (at sea level) we have
observed has boiled at 100 °C.
• Therefore, all water (at sea level) boils at 100 °C.
Inductive argument will never offer 100% certainty!
A typical problem with inductiveargument is that it is
formulated generally, while the observations are made
under specific conditions .
( In our example we could add ”in an open vessel”as well. )

25. 25
An inductive argument have no way to logically (with
certainty) prove that:
• the phenomenon studied do exist in general domain
• that it continues to behave according to the same pattern
According to Popper, inductive argument supports working
theories based on the collected evidence.

26. 26
Counter-example
Perhaps the most well known counter-example was the
discovery of black swans in Australia. Prior to the point, it
was assumed that all swans were white. With the discovery
of the counter-example, the induction concerning the color
of swans had to be re-modeled.

27. 27
MATHEMATICAL INDUCTION
In the empirical inductionwe try to establishthe law.
In the mathematical inductionwe have the law already
formulated. We must prove that it holds generally.
The basis for mathematical induction is the property of the
well-ordering principlefor the natural numbers.

28. 28
THE PRINCIPLE OF MATHEMATICAL
INDUCTION Suppose P(n) is a statement involving an integer n.
Than to prove that P(n) is true for every n ≥n
0
it is
sufficient to show these two things:
1. P(n
0
) is true.
2. For any k ≥n
0
, if P(k) is true, then P(k+1) is true.

29. 29
THE TWO PARTS OF INDUCTIVE PROOF •thebasis step
•theinduction step.
• In the induction step, we assume that statement is true in
the case n = k, and we call this assumption the induction
hypothesis.

30. 30
THE STRONG PRINCIPLE OF
MATHEMATICAL INDUCTION
(1)
Suppose P(n) is a statement involving an integer n. In
order to prove that P(n) is true for every n ≥n
0
it is
sufficient to show these two things:
1. P(n
0
) is true.
2. For any k ≥n
0
, if P(n) is true for every n satisfying
n
0
≤n ≤k, then P(k+1) is true.

31. 31
THE STRONG PRINCIPLE OF
MATHEMATICAL INDUCTION
(2)
A proof by induction using this strong principle follows the
same steps as the one using the common induction
principle.
The only difference is in the form of induction hypothesis.
Here the induction hypothesis is that k is some integer k ≥n
0
and that allthe statements P(n
0
), P(n
0
+1), …, P(k) are true.

32. 32
Example. Proof By Strong Induction
•P(n):n is either prime or product of two or more primes,
for n ≥2.
•Basic step. P(2) is true because 2 is prime.
•Induction hypothesis. k ≥2, and for every n satisfying 2 ≤n
≤k, n is either prime or a product of two or more primes.

33. 33
•Statement to be shown in induction step:
Ifk+1 is prime, the statement P(k+1) is true.
• Otherwise, by definition of prime, k+1 = r·s, for some
positive integers r and s, neither of which is 1 or k+1. It
follows that 2≤r ≤k and 2≤s ≤k.
• By the induction hypothesis, bot h r and s are either prime
or product of two or more primes.
• Therefore, k+1 is the product of two or more primes, and
P(k+1) is true.

34. 34
The strong principle of induction is also referred to as the
principle of complete induction, orcourse-of-values
induction. It is as intuitively plausible as the ordinary
induction principle; in fact, the two are equivalent.
As to whether they are true, the answer may seem a little
surprising. Neither can be proved using standard properties
of natural numbers. Neither can be disproved either!

35. 35
This means essentially that to be able to use the induction
principle, we must adopt it as an axiom.
A well-known set of axioms for the natural numbers, the
Peano axioms, includes one similar to the induction
principle.

36. 36
PEANO'S AXIOMS
1. N is a set and 1 is an element of N.
2. Each element x of N has a unique successor in N denoted x'.
3. 1 is not the successor of any element of N.
4. If x' = y' then x = y.
5.(Axiom of Induction)If M is a subset of N satisfying both:
1 is in M
x in M implies x' in M
then M = N.

37. 37
INDUCTION VS DEDUCTION,
HYPOTHETICO-DEDUCTIVE METHOD
Deductionand inductionoccur as a part of the common
hypothetico-deductive method, which can be simplified in
the following scheme:
• Ask a question and formulat e a hypothesis/educated guess
( induction)
• Make predictions about the hypothesis ( deduction).
• Test the hypothesis ( induction).

38. 38
INDUCTION & DEDUCTION:
AN ETERNAL GOLDEN BRAID
•Deduction, if applied correctly, leads to true conclusions.
But deduction itself is based on the fact that we know
something for sure. For example we know the general law
which can be used to deduce some particular case, such as
“All humans are mortal. Socrates is human. Therefore is
Socrates mortal.”
• How do we know that all humans are mortal? How have
we arrived to the general rule governing our deduction?
Again, there is no other method at hand but (empirical)
induction.

39. 39
In fact, the truth is that even inductionimplies steps following
deductiverules. On our way from specific (particular) up
to universal (general) we use deductive reasoning. We
collect the observations or experimental results and extract
the common patterns or rules or regularities by deduction.
For example, in order to infer by induction the fact that all
planets orbit the Sun, we have to analyze astronomical data
using deductive reasoning.
INDUCTION & DEDUCTION:
AN ETERNAL GOLDEN BRAID

40. 40
INDUCTION & DEDUCTION:
Traditional View

41. 41
GENERAL
PARTICULAR Problem domain
INDUCTION & DEDUCTION:
AN ETERNAL GOLDEN BRAID

42. 42
“There is actually such thing as a distinct process of
induction”said Stanly Jevons; “all inductive reasoning is
but the inverse application of deductive reasoning”–and
this was what Whewellmeant when he said that induction
and deduction went upstairs and downstairs on the same
staircase.”
…(“Popper, of course, is aban doning induction altogether”).
Peter Medawar, Pluto’s Republic, p 177.
INDUCTION & DEDUCTION:
AN ETERNAL GOLDEN BRAID

43. 43
In short: deduction and induction are -like two sides of a
piece of paper -the inseparable parts of our thinking
process.
INDUCTION & DEDUCTION:
AN ETERNAL GOLDEN BRAID

44. 44
FALLACIES
‘
My brethren, I beseech you, in the name of common sense, to believe it possible
that you may be mistaken.’—OLIVER CROMWELL.
What about not properly built arguments? Let us make the
following distinction:
•A formal fallacyis a wrong formal construction of an argument.
•An informalfallacyis a wrong inference or reasoning.

45. 45
FORMAL FALLACIES
“Affirming the consequent"
• "All fish swim. Kevin swims. Therefore Kevin is a fish",
which appearsto be a valid argument. It appearsto be a
modus ponens, but it is not!
• If H is true, then so is I.
• (As the evidence shows), I is true.
• H is true • This form of reasoning, known as the fallacy of " affirming
the consequent" is deductively invalid: its conclusion may
be false even if premises are true.

46. 46
FORMAL FALLACIES
Incorrect deduction when using auxiliary hypotheses • If H and A
1
, A
2
, …., A
n
is true, then so is I.
• But (As the evidence shows), I is not true.
• H and A
1
, A
2
, …., A
n
are all false
• (Comment: One can be certain that H is false, only if one is
certain that all of A
1
, A
2
, …., A
n
are all true.)

47. 47
FORMAL FALLACIES
“Affirming the consequent"
And now again the fallacy of affirming the consequent:
If H is true, then so are A
1
, A
2
, …., A
n
.
(As the evidence shows), A
1
, A
2
, …., A
n
are all true.
H is true
(Comment: A
1
, A
2
, …., A
n
can be a consequence of some
other premise, and not H.)

48. 48
INFORMAL FALLACIES (1)
An informal fallacyis a mistake in reasoning related to the content
of an argument.
•Appeal to Authority
•Ad Hominem (personal attack)
•False Cause
(synchronicity; unrelated facts that appear at the same time coupled)
•Leading Question

49. 49
INFORMAL FALLACIES (2)
•Appeal to Emotion
•Straw Man(attacking the different problem)
•Equivocation (not the common meaning of the word)
•Composition (parts = whole)
•Division (whole = parts) See moreon: http://www.intrepidsoftware.com/fallacy/toc.htm

50. 50
SOME NOT ENTIRELY UNCOMMON
“PROOF TECHNIQUES” •Proof by vigorous handwaving
Works well in a classroom or seminar setting.
•Proof by cumbersome notation
Best done with access to at least four alphabets and special
symbols.
•Proof by exhaustion
Proof around until nobody knows if the proof is over or
not…
READ THE REST ON PAGE 42!

51. 51
REPETITIONS, PATTERNS, IDENTITY
Empirical method relies on observations and experiments,
which lead to a collection of data describing phenomena.
In order to establish a pattern or regularity of behavior, we
have to analyze (compare) the results (data) searching for
similarities (repetitions) and differences.
All repetitions are approximate: the repetition B of an event A
is not identicalwith A, or indistinguishablefrom A, but
only similarto A.

52. 52
CAUSALITY AND DETERMINISM
CAUSALITY
Causality refers to the way of knowing that one thing causes
another.
Practical question (object-level):
what was the cause (of an event)?
Philosophical question (meta-level):
what is the meaning of the concept of a cause?

53. 53
CAUSALITY
Early philosophers, as we mentioned before, concentrated on
conceptual issues and questions (why?). Later philosophers
concentrated on more concrete issues and questions
(how?). The change in emphasis from conceptual to
concrete coincides with the rise of empiricism.

54. 54
CAUSALITY
Hume is probably the first philosopher to postulate a wholly
empirical definition of causality . Of course, both the
definition of "cause" and the "way of knowing" whether X
and Y are causally linked have changed significantly over
time.
Some philosophers deny the existence of "cause" and some
philosophers who accept its existence, argue that it can
never be known by empirical methods. Modern scientists,
on the other hand, define causality in limited contexts (e.g.,
in a controlled experiment).

55. 55
CAUSALITY
What does the scientist mean when (s)he says that event b
was causedby event a?
Other expressions are:
–bring about, bring forth
–produce
–create..
…and similarmetaphors of human activity.
Strictly speaking it is not a thing buta process that causes an
event.

56. 56
CAUSALITY
Analysis of causality, an example (Carnap):
Search for the
cause of a collision between two cars on a highway.
• According to the traffic police, the cause of the accident
was too high speed.
• According to a road-building engineer, the accident was
caused by the slippery highway (poor, low-quality surface)
• According to the psychologist, the man was in a disturbed
state of mind which caused the crash.

57. 57
CAUSALITY
• An automobile construction engineer may find a defect in a
structure of a car.
• A repair-garage man may point out that brake-lining of a
car was worn-out.
• A doctor may say that the driver had bad sight. Etc…
Each person, looking at the total picture from certain point
of view, will find a specific condition such that it is
possible to say: if that condition had not existed, the
accident might not have happened.
But what was the causeof the accident?

58. 58
CAUSALITY
• It is quite obvious that
there is no such thing as the
cause!
• No one could knowall the facts and relevant laws.
(Relevant lawsinclude not only laws of physics and
technology, but also psychological, physiological laws,
etc.)
• But if someone had known,he could have predicted the
collision!

59. 59
CAUSALITY
The event called the cause,is a necessary part of a more
complex web of circumstances. John Mackie, gives the
following definition:
A cause is an Insufficient but Necessary part of a complex of
conditions which together are Unnecessary but Sufficient for the effect.
This definition has become famous and is usually referred to
as the INUS-definition: a cause is an INUS-condition.

60. 60
CAUSALITY
The reason why we are so interested in causes is primarily
that we want either to prevent the effect or else to promote
it. In both cases we ask for the cause in order to obtain
knowledge about what to do.
Hence, in some cases we simply call that condition which is
easiest to manipulate as the cause.

61. 61
CAUSALITY
Summarizing: Our concept of a cause has one objective and
subjective component. The objective content of the
concept of a cause is expressed by its being an INUS
condition. The subjective part is that our choice of one
factor as the causeamong the necessary parts in the
complex is a matter of intere st, and not a matter of fact.

62. 62
CAUSE AND CORRELATION
Instead of saying that the same cause alwaysis followed by
the same effect it is said that the occurrence of a particular
causeincreases the probability for the associated effect,
i.e., that the cause sometimes but not always are followed
by the effect. Hence cause and effect are statistically
correlated.

63. 63
CAUSE AND CORRELATION
X and Y are correlated if and only if:
P(X/Y) > P(X) and P(Y/X) > P(Y)
[The events X and Y are positively correlated if the
conditional probability for X, if Y has happened, is higher
than the unconditioned probability, and vice versa.]

64. 64
CAUSE AND CORRELATION
Reichenbach's principle:
If events of type A and type B are positively correlated, then
one of the following possibilities must obtain:
i) A is a cause of B, or
ii) B is a cause of A, or
iii) A and B have a common cause.

65. 65
CAUSE AND CORRELATION
T
he idea behind Reichenbach’s principle is:
Every real correlation must have an explanation in terms of
causes. It just can’t happen that as a matter of mere
coincidence that a correlation obtains.
73-79 according to: Causes and correlations, Lars-Göran Johansson
http://www.filosofi.uu.se/utbildning/Externt/slu/slultextcc.htm

66. 66
CAUSE AND CORRELATION
We and other animals notice what goes on around us. This helps
us by suggesting what we might expect and even how to
prevent it, and thus fosters survival. |However, the
expedient works only imperfectly. There are surprises, and
they are unsettling. How can we tell when we are right?
We are faced with the problem of error.
W.V. Quine, 'From Stimulus To Science', Harvard University
Press, Cambridge, MA, 1995.

67. 67
DETERMINISM
Determinism is the philosophical doctrine which regards
everything that happens as solely and uniquely determined by
what preceded it.
From the information given by a complete description of the
world at time t, a determinist believes that the state of the
world at time t + 1 can be deduced; or, alternatively, a
determinist believes that every event is an instance of the
operation of the laws of Nature.

68. 68
REPETITION, SIMILARITY
As repetition is based upon si milarity, it must be relative. Two things that are
similar are always similar in certain respects .

69. 69
REPETITION, SIMILARITY
Searching for similarity and differences leads to
classificationsi.e. the division of objects or events in
different groups/classes. The simplest tool by for
classification is the binary opposition or dichotomy
(dualism). When we use dichotomy, we only decide if an
object is of kind A or of kind ∼A. Examples of frequent
dichotomies are yes/no, true/false, before/after, more/less,
above/below, etc.

70. 70
REPETITION, SIMILARITY
Whilst there are no opposites in 'nature', the binary
oppositions we employ in our cultural practices have
developed historically as they help to generate order out of
the dynamic complexity of experience .
At the most basic level of individual survival humans share
with other animals the need to distinguish between own
species and other, safe and dangerous, edible and inedible,
dominance and submission, etc.

71. 71
IDENTITY
• The basic feature of experimental method is its
reproducibility: It must be possible to establish essentially
the same experimental situati on in order to obtain the same
results. This means that the experimental arrangement can
be made with essentially equivalent parts.
• What we call “essentially equi valent”(or we can call it
“essentially the same”) depends on situation. Even here the
principle of information hiding helps us to get a practical
“level of resolution”which means information hiding for
all objects below that level.

72. 72
IDENTITY
So declaring two systems/particles/states as identicalis
entirely the matter of focus. For example if we focus on
question of how many people in this country are
vegetarians, we just treat all people as equal units. If we
want to know how many women in this country are
vegetarian, we discriminate between men and women in
our analysis of people.

73. 73
IDENTITY
We can e.g. also assume that bacteria of particular sort are
interchangeable (indistinguishable) in certain context. That
enables us to make repeated experiments with different
agents and to treat all bacteria of the same type as equal. It
does not mean that they are identical in the absolute sense .
It only means that for our purpose the existing difference
does not have any significance.

74. 74
IDENTITY
• Example of ancient atomic theory. The problem of
showing that one single physical body-say piece of iron is
composed of atoms is at least as difficult as of showing
that allswans are white. Our assertions go in both cases
beyond all observational experience.
• The difficulty with these structural theories is not only to
establish the universality of the law from repeated
instances as to establish that the law holds even for one
single instance.

75. 75
IDENTITY
• A singular statement like “This swan here is white”may be
said to be based on observation. Yet it goes beyond
experience-not only because of the word “white”, but
because of the word “swan”.
• For by calling something a “swan”, we attribute to it
properties which go far beyond mere observation. So even
the most ordinary singular statements are always the
interpretations of the facts in the light of theories !

76. 76
IDENTITY -SAMENESS
•Samenessis therefore a notion deserving of some
attention.
•Ifwe define sameness to be
interchangeability or intersubstitutability of objects
that makes samenessdependent on use.

77. 77
LIKENESS
– The state, quality, or fact of being like; resemblance.
– Synonyms:similarity, similitude, resemblance, analogy,
affinity. These nouns denote agreement or conformity.
Likenessimplies close agreement.
–Similarityand similitudesuggest agreement only in some
respects or to some degree
–Resemblancerefers to similarity in external or superficial
details
–Analogyis similarity, as of properties or functions,
between things that are otherwise not comparable.

78. 78
ANALOGY vs. HOMOLOGY
ANALOGY
1: similarity in some respect be tween things that are otherwise
dissimilar: "the operation of a computer presents an interesting
analogy to the working of the brain"
2: (logic) inference that if things agree in some respects they
probably agree in others
3: drawing a comparison in order to show a similarity in some
respect; "the models show by analogy how matter is built up"
4: a theoretical account based on a similarity between the model
and the phenomena that are to be explained; "it was a computer
simulation of problem solving" [syn: simulation
]

79. 79
HOMOLOGY
1 : a similarity often attributable to common origin
2 a : likeness in structure between parts of different organisms due
to evolutionary differentiation from the same or a corresponding
part of a remote ancestor
b : correspondence in structure between different parts of the
same individual
3 : a branch of the theory of topology.

80. 80
CLASSIFICATION (1)
– A relation is an equivalence relationif it is
reflexive, symmetric and transitive.
– An example of such is equality on a set.

81. 81
EQUIVALENCECLASS
An equivalence class is defined as a subset of the form
{x ∈X : xRa}, where ais an element of Xand the notation
"xRy" is used to mean that there is an equivalence relation
between xand y.
It can be shown that any two equivalence classes are either
equal or disjoint, hence the collection of equivalence
classes forms a partition of X.For all a, b ∈X , we have
aRbiffaand bbelong to the same equivalence class.

82. 82
CLASSIFICATION (2)
CLASSES SHOULD BE DISJUNCT ...
class1
class2
class3
class1:
positive effect
class2:
negative effect
class3: no effect

83. 83
CLASSIFICATION (3)
... AND CHOSEN ACCORDING TO SAME CRITERIA ...
class1
class2
class3
Universehereis a
group of patients
who test a new
medicine.

84. 84
CLASSIFICATION (4)
(INCONGRUITY)
Here is the example of how not to classify.
Jorge Luis Borges in "The language of John Wilkins" Other
Inquisitions 1937-52, explores the incongruity of
classification. He invents a Chin ese classification titled The
Celestial Emporium of Benevolent Recognitions.

85. 85
CLASSIFICATION (5)
Jorge Luis Borges,
"The Analytical Language of John Wilkins"
(a) those that belong to the
Emperor,
(b) embalmed ones,
(c) those that are trained,
(d) suckling pigs,
(e) mermaids,
(f) fabulous ones,
(g) stray dogs,
(h) those that tremble as if they
were mad,
(i) those that resemble flies from
a distance
(j) those drawn with a very fine
camel's hair brush,
(k) innumerable ones,
(l) others,
(m) those that have just broken a
flower vase
Borges's fictive encyclopedia divides animals into:

86. 86
PSEUDOSCIENCE (1)
A pseudoscienceis set of ideas and activities resembling
science but based on fallacious assumptions and supported
by fallacious arguments.
Martin Gardner: Fads and Fallacies in the Name of Science

87. 87
PSEUDOSCIENCE (2)
Motivations for the advocacy or promotion of pseudoscience
range from simple naivety about the nature of science or of
the scientific method, to deliberate deception for financial
or other benefit. Some people consider some or all forms
of pseudoscience to be harmless entertainment. Others,
such as Richard Dawkins
, consider all forms of
pseudoscience to be harmful, whether or not they result in
immediate harm to their followers.

88. 88
PSEUDOSCIENCE (3)
Typically, pseudoscience fails to meet the criteria met by science
generally (including the scientific method
), and can be identified
by one or more of the following rules of thumb:
• asserting claims without supporting experimental evidence;
• asserting claims which contradict experimentally established
results;
• failing to provide an experimental possiblityof reproducible
results; or
• violating Occam'sRazor (the principle of choosing the simplest
explanation when multiple viable explanations are possible); the
more egregious the violation, the more likely.

89. 89
PSEUDOSCIENCE (4)
•Astrology •Dowsing • Creationism •ETs & UFOs •Supernatural • Parapsychology/Paranormal •New Age • Divination (fortune telling) •Graphology •Numerology
•Velikovsky's, von Däniken's,
and Sitchen'stheories
•Pseudohistory •Homeopathy • Healing •Alternative Medicine •Cryptozoology •Lysenkoism • Psychokinesis •Occult & occultism

90. 90
PSEUDOSCIENCE (5)
http://skepdic.com/
The Skeptic'sDictionary,
SkepticalInquirer
http://www.physto.se/~vetfolk/Folkvett/199534pseudo.html The Swedish Skepticmovement (in Swedish)
Scientific Evidence For Evolution
Scientific American, July 2002: 15 Answers to Creationist
Nonsense
Human Genome, Nature 409, 860 -921 (2001)

91. 91
THE PROBLEM OF DEMARCATION (1)
After more than a century of active dialogue, the question of
what marks the boundary of science remains
fundamentally unsettled. As a consequence the issue of
what constitutes pseudoscience continues to be
controversial. Nonetheless, reasonable consensus exists on
certain sub-issues.

92. 92
THE PROBLEM OF DEMARCATION (2)
Criteria for demarcation have traditionally been coupled to one philosophy
of science
or another.
Logical positivism
, for example, supported a theory of meaning which
held that only statements about empirical
observations are meaningful,
effectively asserting that statemen ts which are not derived in this
manner (including all metaphysicalstatements) are meaningless.
Karl Popper
attacked logical positivism and introduced his own criterion
for demarcation, falsifiability
.
Thomas Kuhn
and Imre Lakatos
proposed his own criteria that
distinguished between progressive and degenerative research
programs.
http://www.free-definition.com/Pseudosc ience.html#The_Problem_of_Demarcation

93. 93
CRITICAL THINKING (1)
What is Critical Thinking?
• Critical thinking is rationally deciding
what to believe or
do. To rationally decidesomething is to evaluate claims to
see whether they make sense
, whether they arecoherent,
and whether they are well-founded on evidence, through
inquiry and the use of criteria
developed for this purpose.

94. 94
CRITICAL THINKING (2)
How Do We Critically Think?
A. Question
First, we ask a question
about the issue that we are wondering about.
For example, "Is there right and wrong?"
B. Answer (hypothesis)
Next, we propose an answer or hy pothesis for the question raised.
A hypothesisis a "tentative theory prov isionally adopted to explain certain
facts." We suggest a possible hypothesis, or answer
, to the question
posed.
For example, "No, there is no right and wrong."

95. 95
CRITICAL THINKING (3)
•C. Test
Testing the hypothesis is the next step. With testing, we draw
out the implications of the hypothesis by deducing its
consequences (deduction). We then think of a case which
contradicts the claims and implications of the hypothesis
(inference).
For example, "So if there is no right or wrong, then
everything has equal moral value (deduction); so would the
actions of Hitler be of equal moral value to the actions of
Mother Theresa (inference)? as Value nihilism ethics
claims"

96. 96
CRITICAL THINKING (4)
1. Criteria for truth
Criteria are used for testing the truth of a hypothesis. The
criteria may be used singly or in combination.
a. Consistent with a precondition
Is the hypothesis consistent with a precondition necessary for
its own assertion?
For example, is the assertion "there is no right or wrong"
made possible only by assuming aconcept of right or
wrong -namely, that it is right that there is no right or
wrong and that it is wrongthat there is right or wrong?

97. 97
CRITICAL THINKING (5)
b. Consistent with itself
Is the hypothesis consistent with itself?
For example, is the assertion that "there is no right or wrong"
itself an assertion of right or wrong?
c. Consistent with language
Is the hypothesis consistent with the usage and meaning of
ordinary language?
For example, do we use the words "right" or "wrong" in our
language and do the words refer to concepts and meanings
which we consider "right" and "wrong"?

98. 98
CRITICAL THINKING (6) d. Consistent with experience
Is the hypothesis consistent with experience?
For example, do people really live as if there is no right or
wrong?
e. Consistent with the consequences
Is the hypothesis consistent with its own consequences, can it
actually bear the burden of being lived?
For example, what wouldthe consequences be if everyone
lived as if there was no right or wrong?

99. 99
CRITICAL THINKING (7)
Critical Thinking http://www.criticalreflections.com/critical_thinking.htm
What is truth?
Not a simple question to answer, but this
excellent page at the Internet Encyclopedia of Philosophy
will help show you the way.
http://www.utm.edu/research/iep/t/truth.htm

100. 100
ASSIGNMENT 2
ANALYSIS OF DOWSING, CRITICAL THINKING
• USE THE TEMPLATE GIVEN ON THE COURSE
HOME PAGE
• LEAVE THE TEMPLATE UNCHANGED, WRITE
DOWN YOUR ANSWER AFTER EACH QUESTION.
• THINK CRITICALLY
• SEND YOUR ASSIGNMENTS BY MAIL TO ME.
• IN YOUR MAIL WRITE FOLLOWING
SUBJECT:CT3340 ASSIGNMENT 2