S4002
law, ethics & philosophy of science
jan havliš
: national centre for biomolecular research
:: laboratory of functional genomics and proteomics
Creative Commons: Attribution-Noncommercial-Share alike 3.0 Unported License
workshop part I
philosophy & ethics of science
expanded auxiliary study materials
recommended reading
[01] Karl R. Popper, The Logic of Scientific Discovery, Routledge 2002
[02] Alex Rosenberg, Philosophy of Science, Routledge 2013
[03] Carl G. Hempel, Philosophy of Natural Science, Pearson 1966
[04] Richard Dawkins, Climbing mount improbable, WW Norton 1997
[05] Jaroslav Flegr, Frozen evolution, BookSurge Publishing 2008
[06] Jonathan Marks, What It Means to Be 98% Chimpanzee, Uni Califor Press 2002
[07] Paul K. Feyerabend, Three Dialogues On Knowledge, Blackwell 2011
[08] Paul K. Feyerabend, Against Method, Verso 2010
[09] Victor J. Stenger, God: the failed hypothesis, New York 2007
[10] Stanislaw Lem, Summa technologiae, Uni Minnesota Press 2013
[11] Robert Audi, Epistemology: A Contemporary Introduction, Routledge 2010
[12] Daniel Kahnemann, Thinking – Fast and Slow, Penguin 2012
2
cognition
3
what do you see?
what does it mean to see?
4
: cognitive functions
:: cognitive biases
::: sensorial and rational
: mind and brain
:: senses
::: sight ...
how do you recognise what you see?
what does it mean to recognise something?
: problem of classification and typology
:: sharp or fuzzy categories?
::: degree of similarity
: problem of unambiguous definition
: sensory illusions
: cognitive illusions
how could you be sure about what you see?
: difference between the seen and what you see
:: optics, neurology, psychology
: reality
:: metaphysics of reality
: reality
:: phenomenon vs noise
::: empiricism
:: world exists independently on mind
:: world’s existence depends on creator
:: world exists only in mind
how do you know how recognise anything?
what does it mean to recognise something?
: problem of classification and typology
:: sharp or fuzzy categories?
::: degree of similarity
: problem of unambiguous definition
: sensory illusions
: cognitive illusions
5
how do you explain what you see?
what does it mean to understand?
how do you share the explanation?
how could it happen?
6
: check all possible variants of causalities
:: evaluate the possibility
::: once you eliminate the impossible, whatever remains,
no matter how improbable, must be the truth
: course of actions
:: determinism
: network of actions
: modelling the reality and applying it to explain/understand
: see the difference between description (facts) and explanation (their causalities)
: argumentation
:: thinking : the circular argument, in which theory and proof support each other
: the regressive argument, in which each proof requires a further proof
: the axiomatic argument, which rests on accepted precepts
how do we process the info from around us?
7
: system 1 / rabbit
:: fast, emotional, adaptive, unconscious
: system 2 / turtle
:: slow, rational, non-adaptive, conscious
: system 2 can be trained
: system 1 can be easily fooled
:: in non-adaptive situations
how do we survive in the world?
how does our mind work?
: knowledge acquisition
:: observation, sorting, concluding, predicting, deciding
: mental reflection of world / mental model
:: mental representation of the world and its rules
: knowledge serves
to connect past with the future
: knowledge serves
to successfully deal with challenges
how could the knowledge acquisition fail?
8
: sensory-mental distortion – cognitive dissonance
:: we may not see what is right before our eyes
::: cos we do not know we have to look at it
: too complex problem
:: we may not consider all potential influences
: errors
:: we are fallible and make mistakes
:: we may not be sufficiently educated
: previous, limited knowledge
:: we may not be able to consider new ideas due to lack of preparedness
::: we often do not question old knowledge to move forward
::: we often neglect what we do not find important/interesting
: intuitive acquisition
is more prone to fail
than methodical one
9
Ivan I. Shishkin – Birch forest
oil on canvas
The Tretyak Gallery, Moscow, Russia
basic cognition
we see only through a mirror and in enigmas
videmus modo per speculum in ænigmatibus
cognition is elemental to living
our mind is hard-wired to cognition
: to observe, to sort, to concatenate and to predict
10
visual senses & perception
what does the eye see eye seen brain made
final
picture
= +
visual attention
two focal planes
distant near 11
: pattern recognition
: generalisation
: classification
: sight – primary sense (75 – 90 % of information input)
:: hearing 20 %, smell 5 %, touch 4 %, taste 1 %
: hand – grasping (connection of hand, tongue and frontal lobe)
senses and neurophysiological foundations of cognition
12
how do we recognise specimens?
: how much a different is still the same?
13
what is the same?
what is the similar?
what is the different?
different level of catness
14
universe independent on our
and any mind
universe dependent
on a mind of the (prime) mover
universefullydependent
onone'sownmind
metaphysics
inside
possible natures
of the universe
cogito ergo sum – i think, therefore i am
: is this the only sure thing?
: how do we know there is anything outside?
basic axiom of science
: there is something outside of our mind
and it is independent of it
: we have no reasons to believe in objective reality,
but we have no other option than to act as it exists
David Hume
15
brain in a vat
: is our existence a simulation
or is it reality?
causality and determinism
mechanical
probabilistic
chaotic
cause(s) → phenomenon
: what is the course of actions?
: can it be reverted?
same cause(s) → same phenomenon
same cause(s) → set of phenomena
with different probability
same cause(s) → similar phenomenon
16
: perception
: attention
: memory
: communication
: reasoning
: calculating
: problem solving
: decision making
: language
human cognition
modus vivendi in surrounding environment
originally: hominidae were frugivores, often a prey of predators in savannah
today: solution for perpetually increasing social demands
cogitatio – thinking
17
how does the cognition work?
human cognition model – dual process theory
aka thinking, fast and slow
18
system 1 (rabbit/hare)
: automatic and quick
:: compares similarities and intensities
: intuitive and instinctive
:: no sense of voluntary control
: most of the time, our cognition is driven by system 1
:: rather efficient in adaptive environment, but may fail elsewhere
system 2 (turtle/tortoise)
: effortful and slow (and lazy)
: analytical and objective
:: a feeling of voluntary control
: eventually fixes system 1 failures
:: evolved for non-adaptive situations
: could be trained
science is a superposition to natural cognitive mechanism
: maximises influence of analytical thinking (system 2)
:: by applying methodical cognition
some tools of system 1
intuitive ontologies
: intuitive physics, biology and psychology
: five categories
:: person, animal, plant, artefact, natural object
basic counting
: automatic counting up to 7
advanced counting
: parietal lobe
:: arithmetic
some tools of system 2
analysis
: frontal lobe
language
: complex neural networks
tools of system 2
could be trained!
: things & natural objects
do not move on their own
: plants are alive
but do not move
19
20
time-scale & complexity of phenomena in reality
mixing ethanol + water releases heat & decreases volume
mixing acetonitrile + water absorbs heat & increases volume
: phenomenon takes minutes up to days
Schlumbergera blooms only at 12 – 15 °C
: to see it takes days up to weeks
phenomena regarding sun
: related phenomena take years, up to tens of years
21
apparent movement of Neptune is almost indistinguishable due to very long orbiting time
: such phenomena take decades or centuries
22
10000x
10x
1000x
1000x
a
b
Θ
a = b · tan Θ
observable
phenomenon
phenomenon context
hierarchically
lower
context
hierarchically
higher
context
formal
description
context
a story of three caves (Niaux)
knowledge is contextual
11000 B.C.E. – author
: he or she knew what it means
1660 C.E. – Ruben de la Vialle
: he saw the picture, but was not impressed
: paradigm influences data
: data then retroactively influence paradigm
1971 C.E. – Jean Clottes & Robert Simonnet
: they already knew how significant it is
1864 C.E. – Félix Garrigou
: he guessed the importance of the picture
23
: one real picture exists
: but four different pictures were seen
extended cognition
reflection of world created by means of cognition
: subjective reflection
:: model of world in mind
: intersubjective reflection
:: intersection of subjective reflections
it serves to understand (model) the world around us
: to relate what happened to what is going to happen
cognition + experience = knowledge
24
reality reflected in our mind
can you read the wolf
without experience?
: scientific reflection
:: attempt for methodic, universal explanation
::: method – scientific method
(rather set of methods, none of them is universal)
science is a superposition to natural cognitive mechanism
: maximises system 2 influence by applying methodical cognition
material world
life
mind
culture
objective
subjective
inter
subjective
artefacts
importance of intersubjectivity
: irreplaceable role for a life of man and society
:: creates environment and forms interaction mechanisms
::: law, ethics, religion, economy (money), art
even it exists only in our minds, for a life of an individual is real 25
subjective reflection – mind
: model of the material world
:: cognitive problems of introspection
intersubjective reflection – culture
: intersection of subjective reflections
:: reality apparent & genuine
material world
: objectivity
:: ...per speculum in ænigmatibus
curiosity (+ critical thinking + diligence) = source of knowledge
not nosiness 😉
: the same mind designed a tallow lamp and left a ursine tooth embedded in a rock wall
human mind is able to draw relevant conclusions from incomplete information
26
: emotion recognition efficiency
:: machine 93% vs man 68%
: information is always incomplete (induction), the degree of incompleteness matters
: we decide based on these unsure conclusions intuitively with a confidence
:: we have to, even if information only indicates
:: we pay a high price – (self-)deception
:: deception is effective and must be punished (interaction strategies)
::: strategy of preventing the deception and of reaction to it
:::: e.g. gossip – spreading disrepute of liar and lies
::::: surprisingly more effective than a direct punishment
::: science must uncover deceits (& errors), cos it relies on formerly achieved knowledge
mind – both, rational & irrational
cognitive biases or how system 1 tricks us
: decision-making and behavioural biases
:: deviation when confronted with a specific situation
::: e.g. tendency to judge situation in a way we want to have it
:::: anchoring, power of evidence (fish in a pond)
27
: biases in probability and belief
:: misjudging the probability of phenomena
::: e.g. tendency to think future probabilities are altered by past actions
:::: Russian roulette, Bertrand's box (card) paradox, flipping the coin
:::: Monty Hall paradox
: social biases
:: systematic misjudging of a social situation
::: e.g. tendency to give preferential treatment to members of own group
:::: cognitive arrogance, groupthink, group stupidity
: memory errors and biases
:: the changes in recall of a memory
::: e.g. illusion of memories from before the age of four
:::: suggested truth
if we do not use system 2, this is going to happen
Monty Hall paradox
anchoring
: guess how many African states are in UN
:: with & without spinning the wheel of fortune
Bertrand’s card
paradox
Russian
roulette
: we tend to ignore counter-evidence
:: we rather believe to what approves our opinions
: we tend to create an explanation at any cost, even it is wrong
:: we rather accept quick and dirty explanation that spend efforts thinking
: we tend to move on intuitively
:: we are lazy to use system 2 in planning
: we tend not to see the bigger picture
:: we mistake what we remember for what is
: we tend to eliminate uncertainty (loss) at any cost
:: we rather cheat (risk) than admit an error (loss)
: we tend to be easily mislead by manipulative presentation
:: the first impression makes 70 % of the opinion
: we tend to defend members of our group even we are wrong
:: distortion or withholding of information in a name of social consistency, collective reasoning, selfassigned
higher moral stance, opposition demonisation, insufficient & exaggerated or misleading
responses to critics; high level of group anxiety (groupthink/ group stupidity)
the most important cognition biases applicable in science
28
systems theory
frame for analysis & description of any group of objects, which in concert have a resulting effect
system
: structured set of elements with mutual relations
: manifested to its surrounding differently than a mere set of unrelated elements
system and exchange with surroundings
input
output
surroundings
boundary
system
science ~ applied system theory
29
open
: exchange of material, energetic or information fluxes with surroundings
closed
: exchange of energetic or information fluxes with surroundings
isolated
: isolated from surroundings, no inputs neither outputs into surroundings
set-up
: practical realisation of a system (material system)
hierarchic structure of system
element
: it has input and output (relation, feed-back)
: no inner structure (black box)
exchange inside of the system
: signal (material nature; particle, molecule, sound, etc.)
hierarchy of knowledge
: what? – description of system (descriptive science)
:: what? – outputs of peripheral elements (black box)
::: how? – mechanism of system (how the system works)
:::: why? – system „purpose“ (teleology; from supra-systems properties) 30
supra-system
: cell → organism → evolution
system
: behaviour of the system – graph of output values
: inner element
:: communicates only inside of the system
: outer element
:: communicates inside and outside of the system
sub-system
: may be more complex than a system (plane pilot; to crack nut with laptop)
reductionism (methodical/hierarchical) vs holism
: far east, absence of science
emergence
: the whole is more than its parts
:: from elements/subsystems non-deductible consequences for a system
reductionism is often mistaken with algorithmic compressibility
: de facto determination of information content
: it allows predictions based on application of the algorithm
we can decompose (reduce) observable empirical reality into (sub)systems
: a system, being it-self a sub-/supra-system, can be studied separately
ABFTHJRSCBLOYEAXVY
: algorithmically incompressible
ABCABCABCABCABCNN
: algorithmically compressible – 5x[ABC]+[NN]
31
control and regulation in systems
cybernetics (κυβερνητικός – art of steer, to control)
: science on control and signal transmission inside of the system
: N. Wiener, W. R. Ashby, J. von Neumann & H. von Förster
: today part of informatics
feed-back
: tool of control and regulation
direct (i) or indirect (ii) influence of signal from output on input of element
input output
input output B < 0 = negative feed-back
: impedes; enzyme inhibition
B > 0 = positive feed-back
: amplifies; chain reaction
(i)
(ii)
control
: interference into system
:: no possibility to directly observe an effect
32
scientific model & modelling
: technical
:: how does smth work
we create a model and compare it with reality
: all is fine → ; several different models may behave in a same way
: something is wrong → ☺; we excluded one hypothesis!
: the studied system is always arbitrary
:: it serves to approach complex (holistic) reality by reduction of complexity
: model of the observed
... there are two possible outcomes
: either the hypothesis is confirmed, then you've made a measurement
: or it is not and you've made a discovery ... Enrico Fermi
33
important questions in scientific modelling
: what is an element in a system & what are its relations?
: how deep could i go into a system?
: what is already a vicinity of a system?
: scientific
:: how does NOT smth work
X Y
Y ± vY
X ± vX
deterministic
modelling
stochastic
modelling
models depend on the observed determinism
deterministic modelling
: 100% predictable outcome
: e.g. simple mechanistics...
stochastic modelling
: outcome predictable only with certain precision
chaotic modelling
: special type of deterministic modelling
:: unknown initial conditions
: e.g. non-equilibrium processes
:: attractor
34
an art to recognise, what is studiable phenomenon
: anything could be modelled
example – silent mutations speak
: on genetic level (mRNA translation model) – no change
: on proteomic level – other tRNA causes different folding & thus expression
35
art of a science lies in searching for what's substantial to the system
: phenomenon is never isolated, but always part of supra-systems
what is general (integral to model) and what is special (non-integral)?
... creators of mathematical models
are like crazy tailors; they sew all
kinds of cloth styles hoping, that
some of them could be put on ...
Stanisław Lem
Cys
black box
we know inputs and outputs
: NOT the way of their transformation
enables us to study complex and unknown systems
: we are not dependent on actual state of other field of science
:: scientific „division of labour“
soft modelling
: based on approximation of real function
: hyper-flat of approximated function
:: relation between dependent & independent variables
:: substitution for numeric model
R = f (A, B, C...)
hard modelling
: based on physico-chemical models
36
we make models of relation of dependent variables (R) to independent variables (A, B, C, ...)
thought experiment and paradoxes
: questioning, disproving an old theory
:: evidence by contradiction (reductio ad absurdum)
: proposing a new one
: justification of an existing theory
: Aristotle, Galileo & two connected flying coins
logical a priori procedure of modelling of phenomena
: non-empiric
: intermediate step between postulates and experiment
: substitution of practically impossible experiment
: Maxwell‘s daemon
: Einstein & catching the electromagnetic wave
37
: does the lowest rational number higher than 0 (n) exist?
:: each rational number is divisible by 2
:: thus if n, then also n/2, and thus no n could exists
new levels of cognition
or
research community as meta-scientist
so, are researchers useless? no!
researcher
: low risks (short-term goals)
: basic question asked – how?
→ small shift of knowledge (not enough synthetic work)
scientist
: very high risks (long-term & complex goals)
: basic question asked – why?
→ paradigmatic shifts of knowledge (due to synthetic work)
: distributed cognition
:: ad hoc teaming
::: MMORPG
(massive-multiplayer online role-playing game)
: external cognition
:: records; scientific publications and databases
38
science – quasi-teaming and interaction using external cognition
: researches as well as scientists do matter
epistemology
methodology of science
39
how do you acquire relevant knowledge?
how do you acquire relevant knowledge?
what does it mean science?
40
: acquiring relevant & reliable knowledge
:: testability
:: natural vicissitude of knowledge (paradigm shift)
: universality of knowledge
:: sharing and conveying throughout the knowledge
: scientific method
:: universal & general approach
: asking the right questions (constructing hypotheses)
:: general (disprovable, unverifiable) or particular (verifiable, undisprovable)
: proper testing
:: hypothesis serve to make unique predictions as if true, which have to be tested if they really are
:: hypothesis > prediction > test > reformulation of hypothesis > new prediction > new test ...
::: eventually you cannot disprove it and it remains accepted
how do we conduct science?
how to acquire relevant data?
how to properly evaluate a study?
how to properly share knowledge?
41
: repeatability & reproducibility
: precision & accuracy
: selectivity & sensitivity
: good laboratory practice
: pseudoscience
:: looks like science, but doesn’t play by rule
: statistics
: logic, rhetoric & literacy
: presentation
:: adequacy, brevity, factuality, interestingness
: correlation and dependence
:: is there real causality?
: untested (post hoc) hypotheses
:: p-hacking, HARKing
knowledge (state) = cognition (process) + piece of knowledge (result)
: sensual – innate to all organisms (given by „mechanical“ arrangement)
: non-rational – insight, grasping (not an intellection; intuition)
: rational – innate to all intelligent organisms
(given by the analysis of causes and consequences; theory of mind)
:: scientific – methodical knowledge (given by using the research method)
: extra-sensual – Ψ (yet unknown senses)
vs guess – unclear
vs opinion – biased
vs believing – accepted
knowledge – testable
: reasonable assumption
methodical knowledge acquisition
every piece of knowledge is always connected to another pieces
: Diagoras of Melos
praying & drowned men
:: data relevance
42
43
bringing up a new knowledge
(induction)
using
a composition of a solution
(synthesis)
inferring from knowledge
(deduction)
using
a decomposition of a problem
(analysis)
explication
subdivision of a problem
empiricism
knowledge
assumption
data
formulating a question
generalisation
>
>
>
>
1b.
2.
3.
4.
1b. 2.
3.4.
1a.
1a.
: relational: classification : causal
analysis
synthesis
distinguishing of individual parts (of the system)
: from observable phenomenon to known
connection of signs & meanings of relevant system elements
: from known to unknown
:: approximation of signs and relationships in a common picture
:: approximation to the general from particular phenomenon
: generalisation
: abstraction
algorithm (obligatory) procedure of operation leading to a goal
: scientific method
44
induction
incomplete induction
complete induction
: leads to conclusion, even if we do not know all facts & elements
:: the conclusion is probable, but never sure – inductive leap
: properties of all the same phenomena in one set we study just by observing its subset, because we
cannot study them all at once; it results in uncertainty of conlusions, ie. we make the leap
: impossible in science
:: only in logic and mathematics
: argues from actual data to an inferred model
:: what's the general rule behind it?
P: whenever X then also Y (actual cases)
C: if X then Y (setting the rule; conclusion)
45
F. Bacon
tested subset
of phenomena
the rest of
phenomena set
inductive leap
we bridge it by testing
eliminative induction
method of similarity
if A precedes X, and is independent on others, we consider A to cause X
46
method of difference
if A precedes X and does not precede non-X while others are same, we consider A to cause X
method of joint rule of similarity and difference
if A precedes X, and non-A precedes non-X while others are same, we consider A to cause X
what are the necessary conditions to says it is sunrise or sunset?
method concomitant variation
if A1 & A2 are cause of X1 & X2 and further A2 is cause of X2, then it applies that A1 is a cause of X1
... you know what i'm wondering about, master? every time you leave to check the guards, an order
drops. is it not interesting?
this i'll tell you something even more interesting: whenever he does not leave to check the guards, no
order ever drops ...
play Blaník (Jára Cimrman)
47
method of residuals
if A1 precedes A2 and we find that A1 causes partially (some components of) A2, then also A1 are
cause of remaining components of A2
deduction
P1: if X then Y (general rule)
P2: X (actual case)
C: Y (conclusion)
axiomatic deduction
axiom – it is not a true statement, only a good assumption
: consistence requirement (something is neither possible to verify nor falsify)
: independence requirement (cannot be guaranteed)
: completeness requirement (there is no final theory)
rules are close to the intuitive, pre-logic thinking
: law of non-contradiction (X ≠ A ^ ⌐A)
: law of excluded middle (tertium non datur)
: law of identity (A = A)
: sufficient reason (trueness ~ reasoning; empiricism or derivation from true)
natural deduction
: argues from general principles to specific cases of expected data
:: general rule under the same conditions; true out of true
quack!
?
48
Aristoteles, Descartes
abduction
deduction : derivation of b as a consequence of a
: guarantee of logical consistency
the most frequent method of defunct theory revision
: adaptation to a new finding; pitfalls of maintaining a lifeless theory
:: super-symmetry theory (weak and gravity forces paradox)
::: test did not show presumed consequences, adjust the original theory
new finding might be inconsistent with current theory
: after abduction it is consistently incorporated in a frame of its principles
Peirce, Bateson
: argues directly from actual data to an inferred model
:: is the rule/model also the best explanation? supposed solution for inductive leap
: sometimes used only to generate hypothesis / guess
: without the test, it is not necessarily reliable
P: Y (actual case)
C: if X then Y (belief revision / hypothesis generation)
: check if X (hypothesis test)
49
abduction : derivation of a as an explanation of b
: no guarantee of logical consistency
induction deduction abduction
generalization narrowing
possibilities
new
possibilities
: Hipparchus of Nicaea
and epicycles
analogy
: cognitive process of information transfer
: different from deduction, induction & abduction
: no guarantee of logical consistency
shared abstraction
: analogic objects share principle, template, regularity, attribute, effect or function
: described using comparison, metaphors or allegories
ανάλογος – ratio
: argues about similarity of actual data to other data
:: similar phenomena should have similar rules
analogy = bad science, good pedagogy (lies-to-children)
: originally memetic principle of studying (authority copying)
: may be used to hypothesise, but not as a logical argument
:: Maxwell (physicalisation + mathematisation); electric current
P1: if X ~ A and if Y ~ B (similarity of data)
P2: if X then Y (general rule)
C: if A then B (similarity of rules)
50
identity of relation
: A is to B as C to what?
e.g. „hand is to palm as foot to ...?“
Lakoff, Johnson
anthropic principlehow come there is intelligent life on Earth?
: in philosophy so-called teleologic argument (abductive)
:: out world is non-randomly ordered for us
::: Socrates, Thomas Aquinas, Isaac Newton...
::: William Paley (1802) – blind watchmaker
::: Frederick R. Tennant (1930) – philosophic teleology
: non-random constellation of natural laws leading inevitably to the creation of man
:: role of observer in quantum physics
::: observed seems to be influenced by observer
weak anthropic principle (WAP)
: natural laws need to be so, that they are compatible with origin of life
strong anthropic principle (SAP)
: natural laws are so, that it necessarily leads to origin of life
: in physics and cosmology
:: 1961 – Robert Dicke
:: 1973 – Brandon Carter, term anthropic principle
51
example of abductive thinking
AP – logical tautology (circular reasoning, begging the question)
AP is not a testable scientific hypothesis
: AP is used to derive e.g. prediction that there is no non-C life in universe
:: this cannot test AP – it is neither disprovable, nor justifiable
... if AP applies, then wieners have their slim and long shape to fit a bun ...
Stephen J. Gold
(S)AP is often used as an argument for an existence intelligent designer
: idea of fine tuning of natural laws
:: principle of emergence disproves it
:: it seems, that „main“ constants are not so constant
::: fine structure constant seems to change with time
: results from teleological & social point of view (system 1)
:: humans do things intentionally & tend to see intent in causal chains
52
what is cause & what is consequence?
: cognitive trap causing wrong analysis of data
scientists sometimes hypothesising assume an aim/goal/purpose
: although especially in cosmology and biology they should not
: question remains, what in fact aim/goal/purpose is
i could use
a fur coat
teleology & science
adaptation
: natural selection
:: variability and selection pressure
basic physical constants
: arbitrary numbers
:: not derived theoretically, but empirically
53
absence of aim/goal/purpose
: polar bear did not receive a fur not to freeze
: that bear did not freeze to death, who had a better fur
: thinking, we often use teleological point of view
:: it is cognitive function
::: e.g. term selfish gene
𝐅𝐠 = 𝐆 ∙
𝐦 𝟏 ∙ 𝐦 𝟐
𝐫 𝟐
hypothesis
ὑπόθεσις – assumption, basic
general hypothesis
: for any X there is Y
(ⱯX∈M: if X then Y)
:: unverifiable
::: practically impossible to investigate all cases
:: falsifiable / infirmable
mathematics – „weird“ science; proofs of propositions (Gödel)
in science, mostly just type one hypothesis appears
: all its consequences are tested for conformity with reality
: one contradiction is enough to reformulate the hypothesis
scientific (meta)hypothesis : set of hypotheses (propositions)
in science, there is nothing certain! uncertainty is a function in science, not a defect!
particular hypothesis
: for at least one X, there is Y
(∃X∈M: if X then Y)
:: verifiable
:: unfalsifiable / confirmable
::: similarly as within the general one
54
hypothesis types (basic)
a way we ask questions in science
hypothesis – knowledge searching
: requires theory for formulation
theory – knowledge generalisation
: theory is an undisproved hypothesis
yes no
one-tailed
eats
more
eats
less
two-tailed
hypothesis
what could the question in hypothesis be casted?
what is the relation between food in-take and mood within dogs?
55
56
speculation – basics of premise
: if i assume that X applies, may i use it to construct a hypothesis?
: it's turtles all the way down
new hypothesis / premise
: consistent
:: contains all the parts necessary for explanation & doesn’t these mutually contradictory
:: in the paradigm frame – revolution in science, yes, but not at all costs
: economic
:: does not contain superfluous parts
:: Ockham's razor; not necessarily correct, but the best starting point
: extending & useful
:: it should refine or shift the previous pieces of knowledge
:: it should serve the prediction beyond the frame of hypothesis
: testable (infirmable / confirmable)
:: based on repeatedly observable / testable phenomenon
:: we can determine the conditions of its validity & the conditions of its invalidity
small ad hoc hypothesis
: additional hypothesis (in reformulating) explaining one unexpected aspect of observation
: again, it is not really a logical fail
:: it is suspicious as it may blur the wider context resulting from unexpected consequences
:: synergy of hypotheses – e.g. third Kepler law
big ad hoc hypothesis
: additional hypothesis (in reformulating) tending to explain the disproval of the original one
: it is not really a logical fail, it is rather a proof of evidence bias
:: irrational reluctance to reformulate the original one instead of mere improbable modification
: alien abduction
: where the neutrino came from : dinosaurs hidden in the cave
ad hoc – up to it
: Einstein & cosmologic constant
57
additional (ad hoc) hypotheses
testing H0 /statistical test (test of significance)/
: we presume that hypothesis H0 is valid 😉
:: neutral, unbiased approach; minimisation of cognitive biases
:: without hypothesis, science makes no sense – false positive results
: we decide using which random experiment we shall test the hypothesis
: we assign which random quantity should be the result of the test
random experiment
: its result should not be conclusively predictable from the conditions
: it must be indefinitely repeatable under the same conditions
random quantity
: variable which value is conclusively given by result of the random experiment
58
types of working hypotheses
null hypothesis (H0)
: no dependence between studied variables
alternative hypothesis (Ha)
: dependence between studied variables
59
example of null hypothesis formulation
: assumption – effect A is stronger than effect B
:: H0 – average values of random quantity of effect A and B are equal
: assumption – effect A is under Q stronger than under Z
:: H0 – average values of random quantity of A are under Q and Z equal
: assumption – the stronger effect A, the lower value B
:: H0 – average values of random quantity of B is equal under different values of effect A
null hypothesis testing
: formulating (frequentist/Bayesian) statistical assumption of the test
:: e.g. nature of studied sample, independence of independent variables, values distribution
: defining statistical test, which will be suitable under given circumstances
:: most often used tests – Student t-test, ANOVA, χ2 test, …
: deriving what test statistics T should be under chosen assumptions
:: test statistics T – function, which gives, if H0 is valid, how probable are the measured data
60
test value
critical value
TN
FN
TP
FP
probability of A probability of B
: test p-value (p-value)
:: probability that with H0 valid, the test statistics T would be of a value out of interval <−T,T>
: test confidence level α
:: a number chosen in interval 0 to 1 (0 – 100 %), the lower, the better, usually 0.05
:: risk level, that H0 will be incorrectly rejected, although it is valid (consideration of inductive leap)
:: defines so-called critical region
::: such a part of region of possible values of ​​used random quantity, into which the quantity
result falls with probability α once H0 is valid
: if then p < α, validity of H0 has very low probability
:: result of a random quantity measurement fell into the critical region – H0 rejection, Ha acceptance
: accepting hypothesis H0 means that we consider it possible
: rejecting (falsifying) hypothesis H0 is equivalent to accepting corroborating) hypothesis Ha
:: i.e. observed relations are not by chance
errors in testing
: error type I (false positive results, FP)
:: rejecting H0, although it is valid
: error type II (false negative results, FN)
:: accepting H0, although it is invalid
61
simple case of hypothesis testing
: scientist Henry decides to test if psychics can communicate with the beyond
:: Henry will ask YES/NO questions to which the psychic cannot possibly know the answer,
but he could get them communicating with the deceased
::: p is the probability that the psychic will answer n of his questions correctly
::: Henry chooses to accept that the psychic is communicating with the beyond if the probability
that the psychic answers all questions correctly is less than 0.1% (α)
:::: Henry tries hard to exclude that psychic answers them randomly correct
:::: he calculates that the medium would have to answer 10 questions correctly
:::: if the psychic can do this, it is probably communicating with the beyond (but it is not for sure)
𝐩 =
𝟏
𝟐 𝐧
𝟎. 𝟎𝟎𝟏 <
𝟏
𝟐 𝟏𝟎
p = 0.0009
p < α
α = 0.0010
inhales deeply
thus the null hypothesis
is rejected and
the observed differences
cannot be attributed
to chance alone
: if you torture your data enough, they'll eventually confess
: or search for statistically significant patterns in data, but without hypothesis
:: this is not true testing, does not produce relevant conclusions
:: allows to formulate hypothesis, but it should be tested with new data
:: ends mostly with publishing false positive results
p-hacking
data fishing (data dredging, data snooping)
doi 10.1038/506150a
62
confess!
it is the way i think it is!
the scientist decided to experimentally study one phenomenon (dependence). the first study design
did not work as expected, but the scientist modified the experimental procedures and conducted the
second study. it looked more promising, but still did not give p < 0.05 after data analysis.
the scientist, convinced that he is on track, is collecting additional data. he decided to exclude few
results that looked clearly off. then he noticed that one of his adjustments to the procedure gave a
clearer picture and therefore focused on it. a few improvements to the procedure and the scientist
finally identifies a slightly surprising but really interesting dependence that reaches p < 0.05. the
scientist stubbornly tried to find such a dependence, knowing it was hiding somewhere. he also felt
the pressure to reach the desired p-value.
replication crisis
: methodologically wrong and ethically dubious research
: considered to be the cause of the replication crisis
:: published dependence does not exist de facto, the study cannot be repeated
but there is a catch.
in fact, there was no such dependence. despite a statistically significant result, the scientist published
a false positive result.
the scientist felt that he was using his scientific insight to reveal the hidden phenomenon when he
took various steps after he started his studies: he collected more data. he excluded some data that
looked off. he abandoned his failed attempts and focused on the most promising. he analysed the
data a little differently and made some more tuning.
in fact, he worked methodically incorrectly, unscientifically because he did not fully understand the
scientific method.
63
how to deal with it?
: instead of calculating p, the estimation statistics or bayesian approaches are better
:: the first uses confidence interval, data meta-analysis and effect size analysis
:: the latter then conditional probabilities and interprets probability of original hypothesis
: pre-registration of the study (e.g. at OSF.io) and subsequent publication of the procedure
:: monitoring of the correctness of the study progress
HARKing (hypothesising after the results are known)
doi 10.1207/s15327957pspr0203_4
A reader quick, keen, and leery
Did wonder, ponder, and query
When results clean and tight
Fit predictions just right
If the data preceded the theory
: formulating a priori hypothesis after de facto testing this hypothesis
:: post hoc hypotheses formulated based on data tests, pretending to be original hypotheses
:: denying a disproved a priori hypothesis
: from the outside it seems that all a priori hypotheses are proven and none are refuted
:: the effect of the hunt on positive results
:: considered to be the cause of the replication crisis
: the solution is to change the culture of the publication and the severity test
:: strict type I error checks, statistical power analysis, correct random experiment
a texan sharpshooter aims and fires his gun at target on a barn wall but misses. he
then walks up to the wall, rubs out the initial target, and draws a second target
around his bullet hole in order to make it appear as if he is a good shot.
64
phenomenon
questions
hypothesis 1
discharged battery
hypothesis 2
cracked bulb
springbattery
switch
contacts
light-bulb contact
coversealing
mirror
light-bulb
65
hypothesis 1
discharged battery
hypothesis 2
cracked bulb
premise
exchange of battery
renews the function
premise
exchange of bulb
renews the function
exchange
of battery
exchange
of bulb
test test
rejecting hypothesis accepting hypothesis
experiment
66
reformulation of hypothesis
inconsistent
phenomenon prediction
empirical test
consistent
hypothesis
meta-hypothesis
theory
paradigm
: reformulation of hypothesis
without consequent test
:: ad hoc hypotheses
67
ReaLife science
hypothesising in praxis
with the social consequencies
Ignaz Philipp Semmelweis
: physician, maternity ward, obstetrics
situation
: childbed fever (febris puerperalis)
:: known since antique
:: caused ca 20 % of postnatal deaths of mothers
::: worsened after institutionalised child births in hospitals
observation
: higher mortality in hospital department, where medics-students assisted
: lower mortality with mothers giving birth prior to hospitalisation
hypothesis
: some agent from the dead bodies is the cause
:: students came to the ward directly from dissection exercises
::: recent cases of physicians dying due to injuries during dissections
: thorough hygiene may solve the problem (removing the agent from hands)
:: tested by one group having hands washed with calcium hypochlorite
::: decrease of the infection frequency in one order of magnitude
::: further decrease of general infections after tools sterilisation 68
social aspects
: strong opposition despite immediate improvement (< 2 % cases thereafter)
:: physicians refused the responsibility that they might be the problem
::: physician-gentleman cannot be unclean
:::: washing hands was a social status offence
::: suicides among young physicians, Semmelweis himself collapsed
::: slow progress in implementation of the thorough hygiene (see Max Planck progress principle)
scientific aspects
: Semmelweis was not exactly a good scientist
:: contemporary accepted explanations – dyscrasia (bad humours), miasma (bad air)
::: semi-empirical galenic observations with no testing at all
:: he did not conduct his study systematically and also did not present it properly
::: he used proper time series rate diagrams, but not systematically
::: he wrote no research paper, he just wrote letters to hospital directors
:::: thus he met opposition due to unconvincing scientific study
:: he cannot offer an explanation
::: not his fault, the theory behind microbial infection was not yet fully formulated
:::: another 20 years were necessary
69
70
: distinguishing the repeatable phenomenon from random (noise)
:: we try to find something studiable (we describe)
scientific method
: generalising the relations & predicting related, but yet unobserved phenomenon
:: formalising the relations found and drawing of consequences
::: i.e. what has to happen, if it is so
:: generalising the found relationships carries with it the danger of the inductive leap
: evidence for relations amid such a phenomenon & circumstances under which it occurs
:: we search for causative relations
::: we search for dependencies (attention, these are not correlations)
: test the prediction
:: minimisation of inductive leap
:: in fact we justify or falsify the generalisation
:: if justified, the generalisation means a new piece of knowledge
::: nevertheless, evidence is not a proof
science embodies
science
what it might be?
71
: objectivity
:: repeatable phenomena, no personal preferences
: criticality
:: sceptical & critical evaluation, anything can be questioned
: testability
:: specific ways of empirical testing
:: consequentially so-called self-correction effects
: autonomy
:: maximum independence
::: problematic – social, financial and industrial
: advancement
:: allows cumulative knowledge
set of verified pieces of knowledge
: but (methodically) organised
:: inductive leap consequrnce
...and what it is not
: phlogiston
: combustion
it is always possible to argue HOW,
but not (or only rarely) WHAT
goal of science should be acquiring general knowledge on observable
realism
instrumentalism
to provide true theories about our world
: problem of agnosticism (e.g. deterministic chaos, quantum phenomena)
to provide functional models of observable for predictions
: useful fictions (e.g. equator, electric charge...)
basic goals of science
i.e. using scientific method to model the observable world
: by means of natural laws (models)
reality and truth
reality
: objective reality – independent of our mind/senses
:: but without this assumption, science makes no sense
: intersubjective reality
:: dependent of our minds
truth
: unreachable
:: due to unreachable objective reality
72
παράδειγμα – template, example, model
continual or discontinual increase of knowledge?
paradigm
discourse, hard core
Kuhn model – incommensurable paradigms
: knowledge development – normal science > crisis science > revolutionary science
: paradigms are exclusive
Lakatos model – scientific research programmes
: research programme is broader than paradigm
:: programmes may coexist and compete
:: programmes evolve in times
::: hard core (axioms) stays, protective belt (heuristics) changes
Laudan model – research traditions
: research tradition is also broader than paradigm
:: traditions may coexist and compete
:: much looser connections in-between them
:: acceptance (belief in trueness) or pursuit (deciding if to accept a tradition)
:: pursuit the one with actual highest measure of ability to solve problems
73
new theory does not win through trueness, but plainness
: even the old theory could explicate
:: difficulty to accept new theory due to cognitive biases
:: supporters of the old one must die out or retire
::: so-called Max Planck progress principle – science progresses funeral by funeral
: science easily survives a (black boxes) a do not cause chaos
: it is good to orientate one-self in science one „floor“ up and down
: popularisation is a tool to keep historical scientific memory
discoveries must be done several times (many times), until the scientific community notices them
: theory is a set of (meta)hypothesis, not better evidenced hypothesis
:: they change in time
::: hard core stays, protective bolt is changed
: premise on theory validity (which no one tests)
:: which is not even known or is just too obvious
: John J. Waterston
74
: theories cannot be proven, but should be disprovable
:: within high level of complexity, they cannot be even disproved
: Aristotle and gravity
75
Copernican model
: not much more precise, but simpler
:: Brahean hybrid model
Keplerian model
: explains much more
:: Venus phase
Ptolemaic model
: was imprecise, but sufficient
Mars trajectory on the night sky
Aristarchus vs Ptolemy vs Copernicus vs Brahe vs Kepler
: different descriptions of the same
:: Aristarchus heliocentric model
::: it was widely accepted, but definitely failed
:::: no convincing evidence and later also the lack of will (religion)
plus no pressure to make it more precise
:: Ptolemaic model
::: Alphonsine Tables 1252; precision ≥ 1° → unreliable predictions
:: Copernican model
::: Prutenic Tables 1551; precision ≥ 10’ → also not much unreliable predictions
:: Brahe-Kepler
::: Rudolphine Tables 1627; precision ≤ 1’→ precise measurements gave predictions for ± 200 years
::: Brahe (♁ central, rest revolves )
:::: he still hopes to save old teachings
::: Kepler
:::: correction of Copernicus – elliptic instead of circular orbits
76
earth
aether
fire
air
water Aristotle (– 4th cc) – tópos (τόπος)
: things search for their natural place
:: universe is arranged in levels
:: object is attracted to the level of its substance
Newton (17th cc) – (attractive) force
: law describing dependence of the force on mass and distance
𝐅𝐠 = 𝐆 ∙
𝐦 𝟏 ∙ 𝐦 𝟐
𝐫 𝟐
Einstein (20th cc) – folded space-time
: law describing the force as space-time curvature
𝐑 𝛍𝛎 −
𝟏
𝟐
∙ 𝐑 ∙ 𝐠 𝛍𝛎 + 𝚲 ∙ 𝐠 𝛍𝛎 =
𝟖𝛑 ∙ 𝐆
𝐜 𝟒 ∙ 𝐓𝛍𝛎
scientific models are getting more and more reliable
why do objects fall towards the ground?
77
confusing changes in knowledge paradigm
: scientific models of reality take place out of our everyday experience
:: they change relatively fast and the level of understanding by laymen too
any sufficiently advanced technology is indistinguishable from magic
A. C. Clarke
any technology that does not appear magical is insufficiently advanced
G. Benford
neurophysiology, cognitive psychology; physics of elementary particles
78
: cognitive biases
:: we are prone to erroneous decisions due to how our mind works (system 1 vs system 2)
: inductive leap
:: observations, from which we derive the general rules are never complete
:: natural (general) laws could not be thus proved, only eventually disproved or evidenced
scientists for the sake of practicality neglect the marginal (Ockham's razor)
: it is a voluntary restriction
: may be erroneous, but can be tested
basic methodical limitations of science
precision of measurement is controlled by our requirements for prediction
: there is no absolute precision in any measurement
:: complex model takes too much time making the problem solution impractical
:: simpler approximation takes less time and makes problem solvable
Alexander Calandra &
a story with barometer
empirical tests should not be done only well, but also correctly
: mass of Earth
: quantum chemistry
79
science is not so much a search for truth as it is a refutation of errors
argumentation
80
how do we communicate knowledge?
how does language and culture influences it?
how does the attention influences the information?
81
: cultural influence
:: metaphors
:: mysticism & secrecy vs. rationalism & openness
: language influence
:: language with social prestige
::: Latin, French, German, English
: people like exciting stories more than truth
:: media meet that tendency
: critical reading is a tool which needs learning and practice
: strong cultural context
:: may be blinding and hindering
: how to recognise a proper medium?
how do we do support our conclusions?
how do we convince recipient about our conclusions?
: formal logic
:: unambiguous chain of logical statements
: burden of proof
:: ze who states something is responsible for the proof
: absence of emotion oriented appeals
:: argumentation fallacies
::: hasty or sweeping generalisation
::: non sequitur or post hoc ergo propter hoc
::: ad hominem or ad baculum
::: false analogy
::: false dilemma
::: red herring
::: straw man
::: bandwagon appeal
: our argumentation is as strong
as the weakest statement
82
way of knowledge communication
science – a way to acquire a reliable and correct knowledge on phenomenon – there is no universal one
how do we recognise reliability of the piece of knowledge?
: prediction from theory (theoretical approach)
:: (empiricism) → theory → empirical test of unexpected consequence
: prediction from empiricism (empirical approach)
:: empiricism → theory → empirical test of expected consequence
how do we recognise correctness of the piece of knowledge?
: strong inference
: sorting of alternative hypotheses
: Piazzi, Gauss, Zach/Olbers
:: dwarf planet Ceres
: Fresnel, Poisson
& Arago
:: wave theory of light
: falsification (infirmation) of the actual model/paradigm
: justification of the actual model/paradigm
knowledge = quality data + logical & convincing presentation
: Thales of Miletus & the battle of Halys 585 B.C.E.
:: eclipse prediction supposedly allowed Lydians to win war
83
: use of alternative decision making (two independent methods)
: unsuccessful attempt of model falsification/disproval
argumentation (reasoning, logic)
in experimental sciences, direct way from facts to conclusions does not always exist
why? → insufficient information (incomplete information), novelty
heuristic procedure
: trial search for solutions
:: trial & error approach
reductive judgement
: not logically valid, but acceptable probabilistically
:: in science, there are no irrevocable proofs
inductive judgement
: not logically valid, but acceptable probabilistically
analogic judgement
: not logically valid 84
... if it was so, it might be; and if it were so, it would be; but as it isn't, it ain't ...
Lewis Carroll, Alice in Wonderland and Through the Looking-Glass
basics of argumentation (reasoning)
argumentation is a part of evidence
: facts and their interpretation
argumentation or reasoning
: a way to convince others about our claims
:: not necessarily true ones 😉
: a way to defend your position
: a way to question or refute position you find questionable
85
what if... aha...
assumption hypothesis test
argument
: set of statements following a conclusion, final judgement
argument and its partsstatement
: is a claim, which may be true or false
:: imperative, appeal or questions are not statements
: verifiable
:: qualified by experiment or credible source
: evaluable
:: includes taste or demand interpretation
statement classification
vagueness
: not clear in given context
ambiguous
: at least two meanings in given context
: relevance – evidence should support claim
: representativeness – not just one-sided opinion
: sufficiency – enough evidence, not sporadic one
: accuracy – precise, accurate and up-to-date data (evidence)
: specific
:: exact numbers
: unspecific
:: inexact numbers; easily interchangeable for specific
86
logical analysis of arguments
87
P1: it rains
P2: when it rains, then it is wet
C: (therefore) it is wet
P1: i dropped a bottle of beer
C: (therefore) the bottle broke
purely deductive reasoning
: logically valid
: trueness of P guarantees trueness of C
: deductively invalid
:: no unambiguous conclusion
::: the rule is missing
::: trueness of P does not guarantee trueness of C
P: i dropped a bottle of beer
C: (it is the reason for the fact that) the bottle broke
inductive reasoning
: comes from experience
:: basis for conditional premise
P1: when a bottle hits the concrete, it brakes
P2: when a bottle hits the stone, it brakes
...
composed of two parts
: premise (P)
:: one of the premises is a statement (P1)
:: one of the premises is a rule (P2)
::: arises inductively
: conclusion (C)
a sound argument is formally & contentually correct
88
a valid argument – measure of form
: corresponds to rules for deductive constructions
:: conclusion results from premises
: invalid argument doesn’t necessarily result from premises
: need not to be necessarily true
a true argument – measure of content
: premises are true statements
P1: it snows
P2: if it snows, cats speak
C: thus cats speak
P1: all cats are animals
P2: some cats are black
C: thus some things black are animals
is this a valid, true or sound argument?
P1: Arrhenius supported eugenics
P2: Arrhenius warned against global warming
C: thus all who support idea of global warming are also supporters of eugenics
89
contradiction : both statements cannot be true, but also not untrue (negation test)
possibility : both statements could be true and also untrue
conflict : both statements cannot be true, but could be both untrue
: inside the globe there is another globe much bigger than the outer one
: this dog is white and black
: immigrants do not want to work and they take our jobs
the burden of proof (onus probandi)
: the one who tells, not an opponent
Ockham's razor
: a less complex solution is more probable
Hume's razor
: a lie is more probable than a miracle
Hitchen's razor
: a claim without proof can be without proof dismissed
Popper's razor
: hypotheses are scientific if they can be disproved
correct argumentation
– i have a can of beer.
– really? prove it.
– yes, here it is.
– ah, you're right.
incorrect argumentation
– i have a can of beer.
– really? prove it.
– ha, prove that i don't!
– wtf?
non-existence of evidence is not proof of non-existence
: but you can't prove non-existence anyway
statements (appeals)
logical appeals
: on sense of reason; targets system 2
:: not only evidence, but interpretation makes the cause stronger
: sets the credibility of topic, and thus of disputants
emotional appeals
: on feelings and instincts; targets system 1
:: unconscious reactions – dangerous
: sets the credibility of disputants
social/ethical appeals
: on a sense for right and wrong; targets both systems, but mainly system 1
:: again, unconscious reactions of system 1 make it dangerous
: sets the credibility of disputants
statement evidence
scientist
A correlates w/ B (p = 0.56), under C, D & E
university PR
under certain conditions
there is a relation between A & B
journalists
A causes B, say scientists
conspiracy theorists
A is dangerous and can kill us
90
searching for obviously true statements (empiric support)
+ already supported statements (literary research)
: we cannot repeat the whole history of science
evidence process – logical judgement chain
argumentation fallacies
hasty generalization
: claim made on inadequate evidence
:: youngsters reading violent books are violent, so ban the books
sweeping generalization
: claim made on absolute statement
:: all man are obscene
post hoc ergo propter hoc (after this, therefore because of this)
: claim made on just one thing preceded other
:: immigrants settle city, it suffers from decline, thus immigrants cause it
non sequitor (it does not follow)
: claim made on linking facts with no connection
:: he is blind, so he is unhappy
ad hominen (to the man)
: claim made on personally attacking other disputant
:: he cannot be a good scientist, if he is a micronationalist
91
ad verecundiam (appeal to questionable or faulty authority)
: claim made on a source with arguable credibility
:: dr. smith says that stars are small dots in the sky
begging the question (circular argument, tautology)
: claim made on missing evidence
:: we should cut the science budget due to useless research done
false (or weak) analogy
: claim made on implying two things are similar
:: if we can go to moon, why we cannot cure the influenza
false dilemma (false dichotomy)
: claim made on assumption there are only two possible answers
:: either we ban pornography or the civilisation will continue to decline
red herring
: claim made on distractive statement
:: why to worry about whales, if there is an unemployment here
ad baculum, ad misericordiam (appeal to fear or pity)
: claim made on substituting emotions for reason
:: if you do pass this letter to 10 friends, you will face bad luck 92
straw man
: claim made on oversimplified evidence
:: those who favour gun control cause criminality in the streets
slipper slop (snowball argument, domino theory)
: claim made on suggestion that one thing leads to other negative things
:: vivisection reduces respect for life, and if we do not respect life, we will tolerate violence and
thus we cause end of civilisation. we have to ban vivisection.
burden of proof (appeal to ignorance)
: claim made on not proving something makes it true
:: since you cannot prove that gods do not exist, they exist
equivocation
: claim made on homonymy
:: giving money to charity is right thing, so they have right to it
ad populum (bandwagon appeal)
: claim made on assumed popular support
:: everyone likes watching TV thus it is good
undistributed middle
: claim made on premise, which may or may not overlap
:: all wolves have hair, all men have hair, thus all wolves are men 93
Galileo had frequently leaky argumentation
: and using some data (not necessarily his) lead to testable conclusions
:: he refused Kepler's orbital conclusions (elliptical trajectory) because of Platonic axiom (circular t.)
:: he refused Grassi's comet description (supra-lunar) because of Aristotelian axiom (sub-lunar)
researcher vs quasi-scientist (or anti-scientist, sometimes layman)
: mostly confused, unaware of the power of relevant arguments
: instead of argumentation, they turn to swearing to Scientific Method (hypothesis testing)
:: performing certain rituals, the Method, is rewarded by Truth revelation
:: bear's service to the science
94
researchers often search, who painted that white swan to black rather than why it is black & not white
: scientists never should
: frequent application of post hoc hypotheses
: frequent data fishing – concealment of negative results
: frequent publishing in second rate journals, pre-elected reviewers, etc.
… and social aspects of scientific method
critical thinking (scepticism) requires literacy
: optimally in phonetic script (alphabet) – wider literacy
: records vs oral memory (external cognition)
:: awareness of changes & their easier understanding
superstition is in direct conflict with critical thinking
: methodic incorrect knowledge
:: generalisation & connection of phenomena without shown causality
: Gaius Furius Cresinus (157 B.C.E.)
:: reality of magic
: increased literacy – hussites, woman suffrage
superstition (a magic belief)
Nicolas-Guy Brennet
1777
homeopathic solutions
: 1C = 1 : 102, 10C = 1 : 1020
: analytical grade water 4C (1 : 108)
: 12C – not a single molecule (1 : 1024)
: actually used up to 50000C (1 : 10100000)
95
characters
: up to tens of thousands
alphabet
: up 40 letters
abugida
: hundreds of syllabic letters & ligatures
96
whatweworkw/gainingknowledge?
97
ethics of scientific conduct
misconduct
how may the scientific conduct go wrong?
what should not we do in science?
what to do when discovering scientific misconduct?
98
: questionable research
:: negligence, fraudulence
: questionable publishing
:: also reviewing
: questionable financing
:: spending, fundraising
: are there any tolerable limits?
:: is there a harmless misconduct?
:: is it acceptable to marginalise misconduct?
: is reporting a misconduct a snitch?
: who is responsible for dealing with scientific misconduct?
: tolerance to questionable practices
: not putting one’s interests into other’s way
: internal & third-party research integrity institutions
why do people misconduct in science?
how to distinguish honest error from misconduct?
: psychological pressure
:: internal
:: external
: formally wrong
:: inadequate evaluation and presentation process
::: picture & data manipulation (Corel-ation)
::: statistical manipulation (p-hacking, HARKing)
: contentually wrong
:: apparently wrong inputs
: methodically wrong
:: inadequate methodical approach
: human mind & society
:: evolutionary behaviour mechanisms
::: ambitions ...
::: conformity, social success ...
: critical thinking & argumentation
:: detecting, uncovering, exposing
of fraudulent scientific conduct
99
research process based on the scientific method
distinguishing repeatable phenomenon from random variability (noise)
: so, how to be sure that what we measured is not just „an information noise“
generalise the relations and predict unobserved phenomenon
: correct chaining of circumstance-phenomenon causality
evidence for relation between such phenomenon and circumstances
: hard decision on what is a relevant circumstance and what is not
test the prediction to justify or falsify the generalisation
: empirical arrangement
:: and its evaluation
: compelling arguments in a favour of accepted hypothesis
100
quantitative research types of empirical studies
: studies objectivity
:: observation
:: experiment
advantages
: independent on researcher
: results are general and universal
disadvantages
: prone to study design errors
: prone to biased approach
:: wishful thinking
: studies intersubjectivity and subjectivity
:: anything what's not a quantitative research
qualitative research
advantages
: informed insight into the situation
:: idiographic research
disadvantages
: dependent on researcher
and studied subject(s)
: prone to study design errors
:: no controls possible
mixed research
101
research types
by applicability
102
: basic research
:: scientific knowledge, which serves to acquire new knowledge
:: there is no applied research without it, only engineering
: applied research
:: scientific knowledge transformed into technology
: Mr. Faraday, what is the usefulness of your electromagnetic device?
: Someday you can tax it, Mr. Prime Minister.
an anecdote
ideal case
: researcher is not involved in the process of the observation & does not influence the observed
disadvantage
: experimental objects do have the control over process
empirical test approaches
or basic arrangement of empirical study
observation
: seems more natural
:: historically older in science
:: common in engineering
: descriptive study
: analytical study
103
observation types
analytical study (confirmation)
: case-control study – retrospective search for cause
: cohort study (=incidence, prospective, longitudinal)
:: cohort – set of subjects for which we know actual level of exposition by studied factor
::: we observe the changes after exposition
:: better than a case-control study, but demands lot of subjects (cohorts)
ways of observation
: isomorphic observation (observe as much as possible)
:: the most effective
:: recording technique
objectivity, reliability, validity
104
: reductive description (we observe only what we want to)
:: record sheets, programmes
descriptive study (exploration)
: casuistry – interesting cases (medicine)
: correlation study – appearance of A in dependence on appearance of B
: cross-sectional study – description of subjects and their properties
arrangement
: prospective – we wait what happens
: retrospective – we search back in time
ideal case
: researcher has control over entire process of the experiment, mostly choice of experimental objects
disadvantage
: risk of experimental artefacts
experiment
: intervention study
: easier than observation
:: less demanding on preparation
105
experiment arrangement
simple and complex arrangement
: one study – one factor?
:: single variable vs multiple variables
::: interactions!
basic and experimental set
: representativeness
:: sorted set, generalisation of results
: set homogeneity
:: increases the power of the test
controls
: role of negative and positive (method works) controls
:: controls serve to demonstrate relevance of methods
:: placebo, blind trial, double blind trial, open-label trial
: randomisation
:: unloaded object distribution into groups
let us see, how the
experimental subject will
react on magnetic stimulus...
apply stimulus!
interesting... it looks like
that it resulted in significant
decrease of cardiac activity.
fish seem to be sensitive to
magnetic field.
106
data independence
: pseudo-replicates
:: leaves of one plant
::: 100 trees & one leave, not 2 trees & 100 leaves
:: eggs in nest
::: one egg, more nests
: limit the number of observers, who evaluate only part of subjects
experimental arrangement & data structure
analysis of protein content in tissue
balanced data in individual groups
: in all groups & subgroups is same/similar number and type of objects
groups of
subjects
sample
tissues
parallel
measurem.
replicates
BA
repeated
measurem.
data arrangement
: representativeness, measurement accuracy
:: sampling
:: experimental replicates
: measurement precision
:: technical replicates
107
defining the goal
exploration vs confirmation vs intervention study
: within confirmation & intervention study the hypothesis must be casted before data acquisition
: within exploration study must be clear what will be minimally observed
basic stages in empirical study
108
as most precise hypothesis formulation as possible
: does have the hypothesis sense?
: is it worth the effort and how much effort?
: is not the answer already known?
find the way to answer the hypothesis
: is there a way to answer it?
: is it reasonable that I try to answer it?
types of answers to casted hypotheses
: cross trial yes / no
: asymmetric trials yes / do not know
: heuristics do not know / do not know
: positive // negative results
:: existence // absence of a certain phenomenon
::: controls, power analysis
:: yes // do not know
::: incorrect approach, subjective
: repeating experiments
:: assure your-self vs convince opponents
choice of proper method
: what shall we observe
:: direct and indirect methods, data validity
: how shall we observe it
:: technical performance
109
: with what certainty
:: reproducibility
: with what precision
studies of special interest
: animal testing
:: ethics committee
:: permission to work with animals
: human testing
:: ethics committee – IRB (IEC, ERB)
:: personal data management
:: informed consent
confusing variables
we are not interested in them, they „spoil“ result
: elimination (may decrease representativeness)
: randomisation (representativeness)
: blocking (only within experiments)
: pairing control + object, Latin squares, balanced arrangement
110
errors in acquired data
: stochastic vs systematic error (bias)
:: only by means of systematic error we could explain a false positive result (error type I)
:: source of systematic error – correctable (but also wrongly) by means of randomisation
result evaluation
: statistics, „side“ results, result interpretation
111
: low method specificity vs low method sensitivity
:: false positive vs false negative
:: use of independent method for data acquisition
risks of small sets
: risk of an error of type II
:: false negative results
::: probability of unjustified acceptance of null hypothesis when alternative is valid
: impossibility to block confusing variables
size of a sample set
sufficient size of the sample set depends on
: variability of observed quality
:: low variability allows small sample set size
: number of observed independent factors
:: more factors demand larger size
: technical possibilities and required level of certainty (precision)
risks of large sets
: risk of an error of type I
:: false positive results
::: probability of unjustified rejecting null hypothesis
112
statistical power analysis
: probability of correct rejecting of null hypothesis, if it is invalid
: power analysis equals 1−β, where β is a measure of false negativity
:: as the power analysis increases, probability of error type II decreases
::: probability that we do not discover the dependence even it exists
: determining suitable sample size
: usually taken as reasonable > 0.8 (never < 0.7)
error probability
α – type I
β – type II
power analysis
0.91
H0 Ha
α β 1 – β
113
114
correct data handling
observation value
1 11
2 12
3 11
4 11
5 15
6 10
7 13
average 11.85714...
: reading precision (1 °C; half of the smallest mark, analogue)
12 ± 1 °C
: average
: standard deviation (σ = 1.6)
: outlying values (no outlying one; T-test)
: accuracy (if we have standard; t-test)
:: (arbitrary) real temperature = 11.78542 °C
: limit of confidence (precision; 11.9 ± 1.4 °C) 𝐓 = 𝐓 ± 𝐭 𝛂 ∙ 𝛔 𝐱
even with an imprecise measure we may get „precise“ data
: how Ramsay & Rayleigh discovered argon
:: if the measurement error is 0.03%, difference of 0.48% must be significant
::: difference between unit mass of different nitrogen preparations
: Faraday studied the effect of magnetic fields on emission spectra
:: he saw no effect (1862); Maxwell confirmed it
:: Zeeman (1896) repeated the experiment & found spectral line splitting
::: more accurate spectrometer, but explanation only by quantum physics
::: Faraday had good scientific instincts but inaccurate instruments
: never to forget, which quantities we would like to study and which we in fact did measure
: never to forget to doubt
: take care about everything suspicious (thus potentially interesting)
: never to forget to think
: finish the work!
important remarks to the research process
research is like cross-word puzzle
: we start with what we think is the easiest
: we cross check
:: we erase and re-write
:: we make mistakes or the task is ambiguous
scientific method in research praxis
: general approach leading to logical conclusions, not a particular procedure
: Henri Becquerel
& exposed photographic plates
115
ethics & nature of scientific conduct
nature of scientific conduct
to cut a long story short
: labourer of knowledge
:: often over-specialised, with a limited perspective
: majority of activity are learned on the job
:: soft skills education is more common today
scientist is
: lab technician
: engineer
: teacher
: student
: publicist
: manager
: politician
: collecting unbiased data
: methodically correct evaluation
: formulation of valid conclusions
: results presentation
scientific conduct
116
: working (mostly) within a collective
: community depends of work of others
: positive results are expected from science/scientists
science is first of all a human activity
science is a human activity
scientific knowledge
: uses often intuition and guesses instead of cold reasoning
:: serendipity – role of lucky co-incidence (fortune favours the prepared)
:: heuristics – pure empiricism
: hypotheses are not always tested in a correct manner
:: incorrect test interpretation or conduction
during acquisition of scientific knowledge, the correction appears
: directly by original authors
: or in wider scientific community
: abduction
: ad & post hoc hypotheses!
... without anger and zealotry ...
Tacitus
(sine ira et studio)
117
... scientist has a healthy scepticism,
suspended judgement,
and disciplined imagination ...
Edwin Hubble
basic dangers to scientific conduct
: wishful thinking, egocentrism and intellectual inertia
118
sources of unethical behaviour in science
: career pressure
:: desire to reach the top positions in community hierarchy
::: semi-isolated (scientific) society
:: high level of inner & outer competition
... to recognise failure is an important part of scientific strategy, part of scientific ethics ...
Daniel Friedan
disappointment and fear of failure →
→ sense of threat →
→ unethical behaviour
: social pressure
:: desire to excel & to be useful
: circumstance pressure
:: tendency to ignore unexpected results
:: negligence, haste,
lack of consideration of the circumstances
: personality pressure
:: lowered self-reflection of one's own abilities
:: absence of critical approach to one's own work
:: hypercriticism to the work of others
:: difficult control over conflicting personal traits
cognitive dissonance
119
misconduct vs honest error
disinformation vs misinformationintentional misconduct (fraud)
: discrepancy between reality and statement caused by manipulation
honest error
: discrepancy between reality and statement caused by fallibility
honest errors in science are common, de facto science runs on them
do not be afraid of honest errors in methodic approaches in research
unintentional misconduct (slip)
: discrepancy between reality and statement caused by negligence
pragmatic point of view (why frauds happen not so often, even if you are not overseen)
: uninteresting data are not worth forgery
: interesting data will be reproduced one day
: Woo-Suk Hwang & stem cells
: Columbus & America
scientific misconduct – system characteristics of research community
: N. Lacetera, L. Zirulia, J Law, Economics & Organization 27, 568-603, 2008
: B. C. Martinson, M. S. Anderson, R. de Vries, Nature 435, 737-738, 2005
:: 10 – 15 % of scientists manipulate measured data in their own image
: Daniel E. Koshland
120
: forging data & results
:: starting with complete fabulation up to overlooking critical data (forging)
::: mostly manipulating data & their presentation (doctoring)
unethical conduct in science
(execution, abet and cover)
including anything,
what may happen
on any workplace
conflict of interests
harassment
nepotism
harming the others' interests...
: stealing someone else's data & results
:: to strut in borrowed plumes – reviewers, supervisors... (plagiarism, rogeting)
:: real author is not mentioned as author (ghost-writing)
::: who is and who is not a co-author?
: Schweik's effect (of trade with dogs)
:: publish one paper many times (self-plagiarism)
::: under certain limited circumstances may by ethical
: manipulation, misuse or ignoring of ideas
:: citation amnesia – intentional unquoting of other's work
:: citation fabulation – quoting non-existent or irrelevant papers
: wrong, dangerous or socially unacceptable research
:: unauthorised experiments on humans and animals
::: including manipulation with DNA or cyborgisation
:: uncontrolled weapon development
: Chris Sadler; Roget's Thesaurus
:: left behind → sinister buttocks
p-hacking manual
http://shinyapps.org/apps/p-hacker/
if you ask scientists directly
: 2 % forged results
: 34 % did not work rigorously
peer-to-peer review
: includes version of so-called altruistic punishment
:: keeps co-operating group functional
influence of political and economic power on scientific work
: 96 % of pharmacologic paper authors with positive results are connected to manufactures
: 60 % of authors with neutral results
: 37 % with negative results
project review
: 20 % of project proposals through-put in connection to committee members
:: in less developed countries it is 40 – 60 %
121
if you ask about scientists (indirectly)
: 14 % forged results
: 72 % did not work rigorously
pseudo-science
fringe scienceit would like to be a science, but it does not follow rules
scientific method fringe method
here are facts, what kind
of conclusion we draw?
here are conclusions, what
facts we need to justify
them?
Robert W. Wood – Nature test, 1904
: unseen removed component (aluminium prism)
or put hand in a ray trajectory
:: rays were observed
: assigned observation to cognitive errors
no prestigious journal will publish any important
discovery pre-published without review
N rays (1903)
: Prosper-René Blondlot announced them
: 30 papers in one year
: no-one was able to reproduce the results
quartz
lens
later shade w/ CaS
spark
detector
aluminium window, later prism
Auer
gas
lamp
lead or glass
shade for N rays
122
rules to distinguish fringe from science? it could not be done positively
science: fringe:primary goal knowledge often side effects: ideology, culture, business
knowledge evolves it is not evolving experiments justify only
knowledge is tested testing knowledge is taken as heresy
conflicting data are interesting conflicting data are ignored & suppressed
no knowledge stays once for ever knowledge is often dogmatised
knowledge must stand alone knowledge must be supported by authority
unambiguous language vague & ambiguous language
ID : bible, UFO : anti-establish
science like more new questions than answers
iconoclast in science is hero, not heretic
everyone likes soothsay no one really tests it
supporting facts by scientific degrees
energy, vibration, field
logical impossibility to disprove is not a proof
„pirate code is more what you'd call guidelines
than actual rules“ captain Barbossa
123
pseudo-science has an agenda
cui bono?
why do we deal with pseudo-science?
when we could over-skip it with a smile...
live and let die
desire for pure force affecting our lives
dangerous aspects of pseudo-science
: charlatanry, commercial frauds, cults, politics
: distrust to unknown & over-confidence in simple solutions
: Freud and a case of childless woman
desire to see something causes we see it
and wasn't science once called pseudo-science?
on the break of new paradigm
: A. Michelson, J. J. Thomson, A. Wegener
and could not science become sometimes pseudo-science?
when unable to test new hypothesis
: string theory; an attempt on changing rules of „game“ only to „win“
in science, we don't use the common sense,
but critical thinking and doubts
: Martian channels, Schiaparelli (1877), Antoniadi (1909)
124
origin of pseudo-science? cognitive biases
125
publishing pitfalls of scientific work
there is no cost to getting things wrong.
the cost is not getting them published.
Brian Nosek
: manipulating research authorship
:: co-author has to be able to defend all conclusions
:: honorary co-authorship (no) vs boss inclusion (yes)
::: for less important collaboration use acknowledgement
: manipulating citations
:: purposive behaviour
::: citation gangs, effect of St. Matthew, effect of the Omniscient, citation deflection, ...
: manipulations in peer-reviewed journals
:: „forthcoming“ editors
::: choice of reviewers, ignoring legitimate objections from scientific community
:: private companies co-ordinating fraudulent processes
::: starting with innocent offer to help authors (proof-reading)
::: up to ghost writing of ms from preforms & manipulated contact lists of reviewers
:: corrupting journal editors under false pretend of helping the new authors
:: direct attempts to buy the publishing (sometimes with threats)
: data manipulations
:: fraudulent, but not always conscious behaviour (anti-threat defensive reaction)
::: data forging, stealing, copying, ...
influence of system
: grants, grant committees
:: malversation and clientelism
: peer-to-peer reviews
:: laziness, shallowness
::: probably result of low or null sanctions
: competitiveness
:: may limit the research
::: supresses negative results, requires only those positive
: collaboration
:: should be normal
::: competitiveness and legal norms (companies) may impede
::: anything could be achieved,
if not presented under particular name
influence of society
: political & social choice of research topics
:: limiting the research and its financing
: pop-science
:: science and research commercialization 126
abuse of scientists
: expertise
:: falsifying expert opinion
: un-ethic research
:: under pressure
abuse of knowledge
: knowledge is not good per se
:: but neither evil
:: it is not an excuse anyway
::: power → responsibility
with great power
there must also come great responsibility;-)
: employees respect the law
:: scientist moves on its edge
::: scientific auto-censorship
anonymity
: randomised identifiers of samples and experimental subjects
: never publish non-anonymous data
confidentiality
: if not possible to keep anonymity, than at least confidentiality
consensuality
: honest approach to all eventual experimental subjects
vivisection
: strong rules
:: they alone are not the solution – guilt from suffering
: without vivisection it is not always possible
:: far to complex systems to study
::: HIV monoclonal antibodies, active substances
: consider the responsibility
:: benefit does not ease off responsibility
127
ethics in science
: how to avoid misconduct (ethical dilemmas)
:: knowing the rules
::: their content and also the reason for them
:: knowing the rights and responsibilities
::: co-authorship, conflict of interests, intellectual property, vivisection
:: ability to recognise the most common ethical misconduct examples
::: from other or your own sources 😉
128
: what kind of decisions are in science?
:: how to approach research and its results
::: how to correctly acquire and evaluate data
:::: what to do with unexpected results
::: how to correctly present results
:: how to approach colleagues
:: how the colleagues are supposed to approach you
dr. muff discovered that an octopus is more intelligent than a
cat. under the same conditions, it solved all problems quicker.
garbage in, garbage out (G.I.G.O.)
coincidence 99.21 %
deathtoll
M$
expenses of united states on science, space and technology
suicides by hanging, strangling and suffocation
correlation does not imply causation (often only coincidence)
voodoo correlation – highly sophisticated, but all wrong statistical evaluation 129
130
research
: do not do questionable research
:: it may be questionable due to negligence or fraudulence
: do not tolerate such research in your vicinity
:: e.g. interest groups, which may benefit form it
: do not hinder other's research because of your interests
publishing
: do not publish results of questionable research
: do not allow publication of such results while reviewing
:: negligent or intentionally uncritical reviews
: do not hinder publishing of other's results because of your interests
:: i.e. intentionally and unjustifiably critical reviews
financing
: do not spend grant money on questionable research
: do not allow fundraising for questionable research
: do not hinder fundraising for other's because of your interests
:: fraudulent activities in grant agency's panels
elementary ethical rules in science
131
research (ethics) integrity
integrity in ethics
: commitment, loyalty to certain rules and principles
: strength of character, honourability, honesty, impeccability
official frame for research integrity in Europe & Czechia
: European Charter for Researchers 2005/251/ES
:: EU Official Journal, march 3, 2005
: Ethical research frame
:: resolution of Czech government, august 17, 2005 No. 1005
research integrity in institutions and scientific community
: Rules of Good Scientific Practice, Max Planck Society, 2000
: Good Manners in Science; A Set of Principles and Guidelines, Polish Academy of Science, 2001
: All European Memorandum on Scientific Integrity, Amsterdam 2003
: Ethical code of researchers in AS CR, 2006
: Singapore Statement on Research Integrity, 2010
: Montreal Statement on Research Integrity in Cross-Boundary Research Collaborations, 2013
132
institutions guaranteeing the research integrity
internal
: ethical committees (& ethical codes)
:: ethical committee of MU (art. 14 ethical code of MU, 2008)
:: ethical committee of BUT (art. VII ethical code of BUT, 2016)
external (third-party)
: ethic offices
:: Office of Research Integrity (ORI)
::: created merging several governmental and institutional offices
:: Nævn for Videnskabelig Uredelighed (Denmark)
: independent non-profit organization
:: UK Research Integrity Office (UKRIO)
: scientific journals
:: ethical statements, plagiarism check, sharing information on frauds
The Committee on Publication Ethics (COPE)
: since 1997, advices to editors & publishers on publication ethics
133
difficulties of implementing ethical conduct
ideal norms vs psychological & social reality
: struggle for survival (and resources) – whatever fictive they may seem
:: biological adaptation devised with many survival tools
::: subjective necessity to preserve (or elevate) one’s own status
::: existence of interest & support groups (e.g. citation gangs, management of scientific institutions)
::: practice of deception, selective blindness, doublethink & groupthink
: it's very difficult to point out unethical behaviour (not only in science)
:: often uneasy to evidence when analysing questionable results
::: only reasonable doubt, without empirical verification
:::: sometimes without mere possibility to empirically verify
::: anonymity may help detect, but also cover unethical behaviour
:::: PubPeer vs peer-to-peer review
:: covering unethical behaviour in a name of apparent stability of society by powers that are
::: false loyalty
:: easy misuse of unethical behaviour detection in a struggle of powers
:: intuitive aversion to publish doubts on ethicality of behaviour
::: denunciation, slander vs discontent, information on suspicion
: you have to be either very brazen
or very stupid to get caught at
cheating in science
Peter Gray
Internet Explorer syndrom : ethical problems are not solved when they appear, but when they come out
where is the boundary between ethics and politics?
bioethics
contemporary critique of bioethics
: quandary ethics – rich vs poor (Paul Farmer)
:: too much care vs no care
: bioethics lacks diversity, it is too occident – racism and xenophobia (John Hoberman)
: two necessary cornerstones of bioethics – diversity of ideas and social inclusion (Heikki Saxén)
:: often not fulfilled
: bioethics is based on medicine and biology
: origin in the Hippocratic oath
:: original text is out of date, changes in the reference to the gods, men only teaching, abortions, etc.
:: a follow-on adversarial story about H. refusing a gift from Artaxerxes, an enemy of the Greeks
: today it addresses ethical issues related to the relationship between man and modern medicine
:: euthanasia, abortions, artificial organs, gene therapy, genetic engineering, ...
:: beneficience, autonomy, non-maleficience, human dignity, sanctity of life, ...
: significant influence of politics
:: right to the best treatment – health insurance, price of treatment, availability of treatment, ...
134
135
was it a fraud?
case of David Baltimore, 1986 – 1996
Weaver D., Reis M. H., Albanese C., Costantini F., Baltimore D., Imanishi-Kari T., Cell 45 (1986) 247
David Baltimore (1938)
: biologist, Caltech
1991 – results of fraud investigation are published
: M. O'Toole, subordinate of T. Imanishi-Kari – data were supposedly fully forged
: D. Baltimore defended Imanishi-Kari
: entrance of senator John Dingell, „hunter“ of scientific frauds
:: politicisation & media-promotion
::: Baltimore resigned from all functions
:: civil court denied the cause
::: returned to the Office of Research Integrity
:: animosities between former colleagues
::: T. N. Wiesel has taken over Baltimore's positions & lobbied to expel of Baltimore from scientific
organisations, even to retract his Nobel prize
1996 – Health & Human Services decided, that no fraud happened
: results successfully reproduced by independent laboratory in 1993
: 6500 pages of documents and 6 weeks of closing session
: inexperience and zeal
: professional rivalry (success pressure)
: science witch-hunt by media
probable reasons?
case of Elisabeth Holmes and Theranos, 2003 – 2021
clinical bioanalytics start-up with massive support from celebrities (Kissinger, Clinton, Shulz)
: 19 years old E. Holmes, drop-out from Stanford's School of Engineering (after a year)
:: 2003 – patent for new type of microanalyser of capillary blood (fear of needles)
: start-up Theranos with Edison analyser, > 200 analytes from 5 μl of blood
:: never peer-reviewed technology, no proper technical documentation
:: internal doubts since the start (Tyler Shulz – employee and grandson of director)
:: revealed by John Carreyrou of The Wall Street Journal in 2015
:: analyser never worked, results were taken from classical, „checkout“, measurements
: rocket founding raising, in 2014 value of 9 G$, in 2018 it was 0 $
:: stealth-mode running – no official info going outside
:: EH is facing 11 years sentence for being found guilty of fraud and conspiracy
: 2019 HBO documentary The Inventor: Out for Blood in Silicon Valley
136
interesting publication affairs
Sokal affair
Alan Sokal
*1955, professor of physics at New York University (NYU)
Sokal, A., Transgressing the Boundaries: Towards a Transformative
Hermeneutics of Quantum Gravity, Social Text, 46/47 (1996) 217
accepted 1994-11-28, revised 1995-05-13, published 1996-05-XX
prior to confession, no negative response
confession
: A Physicist Experiments With Cultural Studies,
Lingua Franca, 4 (1996) 62
papers published based on
person of author & sounding, not the correctness or meaningfulness
journal failed peer-review
: ms were read only by editors
137
138
affair of Bogdanov brothers
6 articles in peer-reviewed journals (e.g. Annals of Physics & Classical and Quantum Gravity)
: de facto only two were „original“ – the rest were variation of the two
: soon other scientists find their work fallacious (Max Niedermaier (2002), Urs Schreiber (2004), ...)
Dec 2004, cause Bogdanovs vs journal Ciel et Espace
: Bogdanovs were found guilty in year 2006 to pay court costs (2500 EUR)
:: they never appeared during the trial
public condemnation for overwriting wikipedia entry for their own good
: in France they continue in successful  engagement in science & its popularisation
:: professors on Универзитет Мегатренд (2005), TV show À deux pas du futur (2010)
:: book Avant le Big bang : La création du monde (2004) – 4.3 amazon.fr
fictive institute and internet address in Riga (www.phys-maths.edu.lv)
: Mathematical Center of Riemannian Cosmology (MCRC)
fictive collaborator
: professor L. Yang, Hongkong
: Igor – semiologist, theoretical physicist
: Grichka – mathematician
: Yourievitch Osten-Sacken-Bogdanoff
:: *1949, Ph.D. University of Burgundy, France
Jan Hendrik Schön *1970, Ph.D., Bell Labs
Otto-Klung-Weberbank in physics 2001, Braunschweig prize 2001 & Outstanding Young
Investigator Award of the Materials Research Society 2002
briefly after publication the negative response
: anomalies in data, too precise data, different data had the same noise...
: same graph, different publications @ different conditions
Schön affair
discovery and construction of transistor on molecular level
25 suspicious articles with 20 co-authors
: prestigious journals Nature, Science
: in 36 cases, the frauds were confirmed
: none of co-authors was accused (even his boss Batlogg)
: papers are still cited as evidence (work retracted in 2001 from Nature 37x !)
since 2004 revision on his Ph.D. & DFG relation (banned to apply for grants)
: 2009 title revision approved by university authorities, 2010 lawsuit with university
:: local court reversed decision of the university
: 2011 state court & 2013 federal administrative court recognized decision of the university
: 2014 federal constitutional court finally recognized decision of the university; title taken 139
Science Nature
bifenyldithiol
alkandithiol
Bohannon affair
who's afraid of peer review?
: Science 2013
fictive authors
: e.g. Ocorrafoo Cobange, Wassee Institute of Medicine, Asmara, Eritrea
manuscripts sent manuscripts rejected manuscripts accepted
304
total
98
total
157
total
Beall's list
DOAJ
in both
good review
slack review
no review
DOAJ – directory of open access journals
molecule X of lichen of Y specie inhibits growth of cancer cells type Z
: database of X, Y & Z + programme à la MadLibs in Python
:: D. Aguayo, M. Krohn, J. Stribling – SCIgen + SCIpher (2005); 85 papers went through
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Sokal affair squared
Alan Sokal pointed out on questionable publishing
: 1996 – Transgressing the Boundaries : Towards a Transformative Hermeneutics of Quantum Gravity
James Lindsay, Helen Pluckrose & Peter Boghossian tried it again
: 2018 – 20 fake papers of fashionable jargon & ridiculous conclusions tried to high-profile journals
:: fancy fields including gender or obesity studies
:: 4 get published, 3 accepted, 4 under review and 9 rejected
:: debunked by media (The Atlantic, NYT and the Economist)
: it received mixed reactions
:: praise for exposing the weakness of scientific media for sensational anti-system articles
::: Stars, Planets, and Gender: A Framework for a Feminist Astronomy
::: Super-Frankenstein and the Masculine Imaginary: Feminist Epistemology and Superintelligent
Artificial Intelligence Safety Research
:: criticism for non-scientific approach & pointlessness
::: as an experiment it was poorly designed, at least there were no controls
::: peer review can hardly discover fraud, unless there are flagrant discrepancies in the paper
case of Radek Zbořil
high profile nanomaterial scientist at Palacký University of Olomouc
: The Regional Centre of Advanced Technologies and Materials (RCPTM)
: H-index > 60, one of the most cited researchers in Czechia
: spectra manipulation in his 2007 JACS article revealed by student in 2012
:: it was ignored and the student has failed defence (7 negative)
:: new defence in 2013 again failed (2 positive, 2 negative, 2 invalid)
: new dean in 2018 started the process of re-evaluation of the 2012 revelation
:: fraudulent behaviour was found with the highest possible probability
:: dean submitted it to rector and he to ethical committee, which agreed with the previous findings
:: JACS refused errata offered by authors and retracted the paper
: new manipulation with data was found in 2016 Nature Communication paper in 2019
:: errata submitted contained another picture manipulation
::: questionable defence of the authors vs rigorous arguments of the opponents
:::: mathematical analysis of the noise in new spectra showed that they were manipulated
:::: internal fight lead by powerful clique of bosses of research centre against faculty
:::: ethical committee chair resigned in a protest (being falsely accused of fraud)
unfortunately, this sad show is still going on due to absence of an independent, third-party evaluation
original
manipulated
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difference between different publication affairs?
Schön & Bogdanovs & Zbořil & Sokal's epigones (and probably Bohannon too) were exposed
: not due to reviews, but application of scientific method (reproducing & critical analysis)
:: self-correcting tendencies in science
Sokal wouldn't be exposed, if he did not confess
: pseudo-science only piles texts & concepts without possibility to test them
:: PS. philosophy needs necessarily not to be pseudo-science
negligible sanctions for a wrong review lead to slack reviews
manipulations in peer-reviewed journals
: „forthcoming“ editors – choice of reviewers, ignoring legitimate objections
: private companies – co-ordinating fraud processes
:: starting with innocent help to authors (proof-reading)
:: up to ghost writing of manuscripts from preforms
and manipulated contact lists for reviewing process
(true names of offered reviewers, fake contacts)
:: corrupting journal editors under false pretend of helping the new authors
:: direct attempts to buy the publishing (sometimes with threats)
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duel with paper mills
paper mills
: commercial institutions producing fraudulent papers on demand
:: an agent mediates between the customer and the mill
:: 14 800 USD for first-author paper in Int. J. of Biochemistry & Cell Biology
::: two particular co-authors 26 300 USD
:: from contract to publication – few weeks
:: after making it public, the company denied everything
: up to 27 suspected companies on the Chinese market
:: people are sparing no expenses in order to get published in SCI – one of the customers
year
numberofpapers(thousands) Chinese publication boom
: world 2.5x more papers
: China 6.3x
: blog
:: retractionwatch.com
:: since Aug 2010, I. Oransky, A. Marcus
: informs about malpractice
:: retracted articles
::: mistake vs fraud
:: publishing fraud
:: publishing misconduct
: self-correcting processes in science
:: to cheat is worth only for a short term
:: up to 100% chance to be discovered
: dark side of fighting the unethical publishing
:: blackmailing authors under the pretext of revising articles
:: predatory companies pretend to be guardians of research integrity 145
retraction watch
: Scott S. Reuben
:: 21 articles retracted
:: sentenced
::: 3 years of supervised release
::: 400 000 USD to pay
: Gregg L. Semenza
:: picture manipulation
:: Nobel prize (2019) replication crisis
: email with flattery, e.g. how damn significant they found one of your papers
:: irrelevant, often it is your fresh paper with no citations
: proposal to publish in a special issue or even editing it
:: pseudo-open access, misusing the idea of open publishing
: after acceptance (without review) follows an info on high price to be paid
:: regarding some unannounced service
: more ethics violations
:: editorial board without their consent, false identity (ISSN)
: Beall's List (https://beallslist.weebly.com)
: Stop Predatory Journals (https://predatoryjournals.com)
: The Journal Blacklist, Cabells (paywall; 2019 – 12 000 records)
predatory practicespredatory journals
predatory conferences
: email asking you to give a lecture on „an important“ conference
:: importance is fictive, often obscure location
: generous offer of significant discount (ca 300 USD)
: thereafter hefty bill comes (ca 1600 USD)
: misleading homepage
: OMICS Group, Coltharp Institute...
cure for predatory practices
: publisher's honesty
: honest business practices
: transparency of processes
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commercialised science
: decisive are pseudo-objective criteria, not an experienced opinion
:: tends to reduce the personal interests, but is forced into line (fashion)
::: tit-for-tat, subconscious siding with people with the same opinions
: permanent (tenure) vs temporary job
:: modern feudalism
: grants go the old guard, the young often get nothing
: opportunists/careerists often do science, who seek glory and power
:: in science it works only by making progress – in small steps
the greatest issues of science today
its connection to state
: need for large investments leads to new sources
:: state or private sector
:: power ennobles, absolute power ennobles absolutely
: science is hired
:: controlled by immediate political & economic interests
::: political and industrial lobby 147
syndrome of the only game in town
(book by Kurt Vonnegut jr.)
: actual situation in particle and theoretical physics
:: combining pop-science with difficulties to find alternatives to it
: consequences
:: scientific mafia propagating pop-science and supressing alternatives
:: the old guards sitting on concepts and money
::: before 1970 professor age median was 40, in 2002 it is already 60
::: only ca 1/8 with Ph.D. get job in their field
story of the book Not even wrong by Peter Woit
: intentionally negative reviews & absence of will to accept criticism (Luboš Motl)
... [hypothesis infirmation] is some kind of papal trial, a popperazzi, about what science is and what not ...
Leonard Susskind
postmodern plurality of theories
: reassessment of what it means that the theory meets experiment
:: „invisibility“ of quark
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main source of problems
: scientific illiteracy
:: among laymen (non-scientists)
:: but also experts (not only from humanities)
→ alienation of science, their results and scientists in society
science reception by general public
149
sources of scepticism & disbelieve towards science
not to explain vs to explain incorrectly
: major mistake of science critics (god of the gaps)
:: where is no explanation, there is a gap for „a god“
: incompleteness of knowledge
: its negative consequences
: unintelligible language of science
: subjective insecurities & nesciences
often disillusion in general public, who expected something of science, what was not fulfilled, and
they thus somehow conclude, that science becomes obsolete and that scientific way of studying
should be substituted by something else
confusing changes in a paradigm
: scientific models of reality take place out of our everyday experience
:: they change relatively fast & the level of understanding decreases
scientists are unable to admit things
they are unable to explain
: a wrong map is better than no map at all