Comprehensive summary of all material for Science Theory. It concerns all articles, the book Philosophy of Science (chapter 1 to 5) and extensive notes of all lectures of 2021 for the course.
Philosophy of Science
By Samir Okasha
Chapter 1: What is Science?
What common feature all the things on that list share (i.e. what it is that makes something a science).
- Distinguishing features of science lie in particular methods scientists use to investigate the
world (e.g. use of experiments).
- Construction of theories.
The origins of modern science lie in a period of rapid scientific development that occurred in Europe
between the years 1500 and 1750: the scientific revolution.
- Aristotelianism
All earthly bodies are composed of just four elements: earth, fire, air, and water.
- Copernican revolution
In 1542 Nicolas Copernicus (1473-1543) published a book attacking the geocentric model of the
universe, which placed the stationary earth at the centre of the universe with the planets and the sun
in orbit around it. Geocentric astronomy (also known as Ptolemaic astronomy) lay at the heart of the
Aristotelian world-view. Copernicus suggested an alternative: the sun was the fixed centre of the
universe, and the planets, including the earth, were in orbit around the sun. Earth loses the unique
status.
- Johannes Kepler (1571-1630) and Galileo Galilei (1564- 1642)
Kepler discovered that the planets do not move in circular orbits around the sun, but rather in
ellipses: `first law' of planetary motion. Second and third laws specify the speeds at which the planets
orbit the sun. Galileo: telescope discoveries (e.g. mountains on the moon, a vast array of stars, sun-
spots, and Jupiter's moons). All freely falling bodies will fall towards the earth at the same rate,
irrespective of their weight (heavier land earlier simply due to air resistance). Freely falling bodies
accelerate uniformly (i.e. gain equal increments of speed in equal times): law of free-fall. Galileo as
the first truly modern physicist: first to show that the language of mathematics could be used to
describe the behaviour of actual objects in the material world. Emphasis on the importance of testing
hypotheses experimentally.
- Rene Descartes (1596-1650)
Developed `mechanical philosophy': the physical world consists simply of inert particles (corpuscles)
of matter interacting and colliding with one another. Explain all observable phenomena in terms of
the motion of inert, insensible corpuscles.
- Isaac Newton (1643-1727)
Mathematical Principles of Natural Philosopky (1687): the universe consists simply of particles in
motion. Every body in the universe exerts a gravitational attraction on every other body; the strength
of the attraction between two bodies depends on the product of their masses, and on the distance
between them squared. The laws of motion specify how this gravitational force affects the bodies'
motions. Invented `calculus'.
- Early years of the 20th century
Relativity theory (Einstein): showed that Newtonian mechanics does not give the right results when
applied to very massive objects, or objects moving at very high velocities.
Quantum mechanics: shows that the Newtonian theory does not work when applied on a very small
scale, to subatomic particles.
Charles Darwin: theory of evolution by natural selection (1859): contemporary species have evolved
from ancestral ones, through a process known as natural selection. Natural selection occurs when
some organisms leave more offspring than others, depending on their physical characteristics; if
these characteristics are then inherited by their offspring, over time the population will become
,better and better adapted to the environment. Over a large number of generations it can cause one
species to evolve into a wholly new one.
Molecular biology (in particular molecular genetics): Watson and Crick (1953) discovered the
structure of DNA, the hereditary material that makes up the genes in the cells of living creatures.
How genetic information can be copied from one cell to another, and passed down from parent to
offspring, thereby explaining why offspring tend to resemble their parents.
Cognitive science: studies various aspects of human cognition (e.g. perception, memory, learning,
and reasoning). The human mind is in some respects similar to a computer. Human mental processes
can he understood by comparing them to the operations computers carry out.
What is philosophy of science?
The principal task of philosophy of science is to analyse the methods of enquiry used in the various
sciences. Question assumptions that scientists take for granted.
Science and pseudo-science
Karl Popper: the fundamental feature of a scientific theory is that it should be falsifiable. The theory
makes some definite predictions that are capable of being tested against experience. If these
predictions turn out to be wrong, then the theory has been falsified, or disproved. Otherwise:
pseudo-science. Scientists do not just abandon their theories whenever they conflict with the
observational data. Usually they look for ways of eliminating the conflict without having to give up
their theory. Virtually every theory in science conflicts with some observations.
Ludwig Wittgenstein: there is no fixed set of features that define what it is to be a ‘game’. Rather,
there is a loose cluster of features most of which are possessed by most games. But any particular
game may lack any of the features in the cluster and still be a game.
Chapter 2: Scientific Reasoning
Deduction and induction
Deductive reasoning/deductive inference (e.g. all Frenchmen like red wine, Pierre is a Frenchman,
therefore, Pierre likes red wine). The first two statements are called the premisses of the inference,
while the third statement is called the conclusion. If the premisses are true, then the conclusion must
be true too: the premisses of the inference entail the conclusion. Is safer dan inductive reasoning.
Inductive reasoning/inductive inference (e.g. the first five eggs in the box were rotten, all the eggs
have the same best-before date stamped on them, therefore, the sixth egg will be rotten too). The
premisses do not entail the conclusion: does not guarantee that the sixth egg will be rotten too.
Move from premisses about objects we have examined to conclusions about objects we haven't
examined (e.g. the fact that the DS sufferers in the sample studied had 47 chromosomes doesn't
prove that all DS sufferers do).
The word `proof’ should strictly only be used when we are dealing with deductive inferences. Popper:
claimed that scientists only need to use deductive inferences. Although it is not possible to prove
that a scientific theory is true from a limited data sample, it is possible to prove that a theory is false.
Hume's problem
David Hume (1711-1776): the use of induction cannot be rationally justified at all. We use induction
all the time, in everyday life and in science, but this is just a matter of brute animal habit. If
challenged to provide a good reason for using induction, we can give no satisfactory answer.
Whenever we make inductive inferences, we seem to presuppose the `uniformity of nature' (UN):
depend on the assumption that objects we haven't examined will be similar, in the relevant respects,
to objects of the same sort that we have examined. We cannot strictly prove the truth of UN. To
argue that induction is trustworthy because it has worked well up to now is to reason in an inductive
way. Hume: our inductive inferences rest on the UN assumption. But we cannot prove that UN is
true, and we cannot produce empirical evidence for its truth without begging the question. So our
inductive inferences rest on an assumption about the world for which we have no good grounds. Our
confidence in induction is just blind faith. Criticisms:
- Probabilities instead of proof.
, - Induction is so fundamental to how we think and reason that it's not the sort of thing that
could be justified. Peter Strawson: Induction is one of the standards we use to decide
whether claims about the world are justified (e.g. worrying of the law is legal).
Inference to the best explanation
Although we cannot be certain that the hypothesis is true, on balance it looks quite plausible: it is the
best way of accounting for the available data= `inference to the best explanation' (IBE). Gilbert
Harman: IBE is more fundamental: whenever we make an ordinary inductive inference (e.g. all pieces
of metal examined so far conduct electricity, therefore all pieces of metal conduct electricity) we are
implicitly appealing to explanatory considerations. We assume that the correct explanation for why
the pieces of metal in our sample conducted electricity, whatever it is, entails that all pieces of metal
will conduct electricity. But if we believed that the explanation for why the pieces of metal in our
sample conducted electricity was that a laboratory technician had tinkered with them, we would not
infer that all pieces of metal conduct electricity. Proponents: think that ordinary induction is
ultimately dependent on IBE or IBE is itself parasitic on ordinary induction. When we try to decide
which of a group of competing hypotheses provides the best explanation of our data, we invariably
appeal to knowledge that has been gained through ordinary induction.
The best explanation is the simplest or the most parsimonious one.
Probability and induction
The frequency interpretation of probability (e.g. the probability of a male smoker developing lung
cancer is 1 in 4: means that a quarter of all male smokers develop lung cancer): equates probabilities
with proportions, or frequencies. In principle, we should be able to assign a precise numerical
probability to each of the statements about which we have an opinion, reflecting how strongly we
believe or disbelieve them. The subjective interpretation of probability implies that there are no
objective facts about probability, independently of what people believe.
The logical interpretation of probability: a statement such as `the probability of life on Mars is high' is
objectively true or false, relative to a specified body of evidence. A statement's probability is the
measure of the strength of evidence in its favour, on this view. Advocates of the logical
interpretation think that for any two statements in our language, we can in principle discover the
probability of one, given the other as evidence (e.g. probability that there will he an ice age within
10,000 years, objectively true due to evidence of global warming). Philosophers of science are
interested in probability for two main reasons:
1. In many branches of science (especially physics and biology) we find laws and theories that
are formulated using the notion of probability. The need to understand the scientific laws
and principles is an important motivation for the philosophical study of probability.
2. The hope that it might shed some light on inductive inference. It’s tempting to suggest that
the premisses of a typical inductive inference do make the conclusion highly probable.
Hume's problem probable answers:
- The frequency interpretation: to say that a very high proportion of all objects obey a
law we have to use induction because we have only examined a tiny fraction of all
the objects in the universe. Knowledge of probabilities becomes itself dependent on
induction.
- The subjective interpretation of probability: if there are no objective facts about
probability, then we cannot say that the conclusions of inductive inferences are
objectively probable.
- The logical interpretation of probability: regarding a statement's probability as a
measure of the evidence in its favour, as the logical interpretation recommends,
tallies neatly with our intuitive feeling that the premisses of an inductive inference
can make the conclusion highly probable, even if they cannot guarantee its truth.
Chapter 3: Explanation in Science
One of the most important aims of science: try and explain what happens in the world around us.
Goals:
, - Practical ends (e.g. we might want to know why the ozone layer is being depleted so quickly,
in order to try and do something about it).
- Scientific explanations: to satisfy our intellectual curiosity - we want to understand more
about how the world works.
Hempel's covering law model of explanation
Scientific explanations are usually given in response to 'explanation-seeking why questions' (e.g. why
is the earth not perfectly spherical?, why do women live longer than men). They are demands for
explanation. To give a scientific explanation is to provide a satisfactory answer to an explanation-
seeking why question.
Hempel: scientific explanations typically have the logical structure of an argument (i.e. a set of
premisses followed by a conclusion). The conclusion states that the phenomenon that needs
explaining actually occurs, and the premisses tell us why the conclusion is true. The task of providing
an account of scientific explanation then becomes the task of characterizing precisely the relation
that must hold between a set of premisses and a conclusion, in order for the former to count as an
explanation of the latter (e.g. why does sugar dissolve in water). Hempel's answer to the problem
was three-fold:
1. The premisses should entail the conclusion (i.e. the argument should be a deductive one).
2. The premisses should all be true.
3. The premisses should consist of at least one general law (e.g. all metals conduct electricity). `
`laws of nature'.
Hempel: to explain a phenomenon is to show that its occurrence follows deductively from a general
law, perhaps supplemented by other laws and/or particular facts, all of which must be true (e.g.
explaining the death of the plant by deducing it from two true laws - that sunlight is necessary for
photosynthesis, and that photosynthesis is necessary for survival - and one particular fact - that the
plant was not getting any sunlight: given the truth of the two laws and the particular fact, the death
of the plant had to occur).
Hempel's model of explanation:
- General laws (eaplanans)
- Particular facts (eaplanans)
Phenomenon to be explained (explanandum): may be either a particular fact or a general
law
The essence of explanation is to show that the phenomenon to be explained is `covered' by some
general law of nature.
Hempel: the relation between explanation and prediction are two sides of the same coin. Whenever
we give a covering law explanation of a phenomenon, the laws and particular facts we cite would
have enabled us to predict the occurrence of the phenomenon, if we hadn't already known about it.
Every scientific explanation is potentially a prediction - it would have served to predict the
phenomenon in question, had it not already been known. Every reliable prediction is potentially an
explanation.
The problem of symmetry
Hempel’s model allows something to count as a scientific explanation that obviously is not (e.g.
explaining why a flagpole is 15 meters because it’s shadow is 20 meters by trigonometric calculation
someone just chose to build it that high). If x explains y, given the relevant laws and additional
facts, then it will not be true that y explains x, given the same laws and facts: explanation is an
asymmetric relation. Information that serves to predict a fact before we know it does not serve to
explain that same fact after we know it.
The problem of irrelevance
The covering law model is again too permissive: it allows things to count as scientific explanations
that intuitively are not (e.g. explaining why a man is not pregnant because he takes birth control
pills). A good explanation of a phenomenon should contain information that is relevant to the
phenomenon's occurrence (e.g. man is not pregnant because he can’t become pregnant, not because
he is taking birth control pills).
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