LOGIC AND CRITICAL
THINKING
Lecture 11 – Scientific Reasoning I
REVIEW

First, let’s go over homework from last time.
REVIEW
Last time we talked about the relationship
between correlation and causation.
 If we know that two events co-occur – if they are
correlated – that is some evidence that one might
cause the other.
 But, there are multiple possible explanations of
correlations – there are several different
relationships the two events might have.

REVIEW

If event A is correlated with event B, any of these
are possible:

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There is no causal relationship between A and B
A causes B
B causes A
A and B form a causal loop
A is a minor cause of B (or B is a minor cause of A)
A and B share a common cause, C.
A is an indirect cause of B, via a side-effect C (or B is
an indirect cause of A)
REVIEW

We also went over several different fallacies
related to poor causal reasoning:
The POST HOC fallacy occurs when someone
concludes that A causes B without any evidence other
than the fact that A comes before B.
 Confusing CORRELATION with CAUSATION occurs
when someone knows that A and B are correlated,
but jumps to the conclusion that A caused B.
(usually this involves neglecting a common cause)
 REVERSING CAUSAL DIRECTION occurs when
someone assumes that A caused B, while neglecting
the possibility that B caused A.

REVIEW

We also went over several different fallacies
related to poor causal reasoning:
The SINGLE CAUSE fallacy occurs when someone
takes a complex causal situation and oversimplifies
it – looking at only one cause, when really there are
many.
 The GENETIC fallacy is a little different – it doesn’t
make a mistake about what caused an event, but
instead it makes a mistake about how important the
cause is. The genetic fallacy involves attributing a
feature of the cause to the effect – for example,
claiming the effect is bad because the cause is bad.

SCIENCE
Today we begin talking about scientific reasoning.
The goal for today is to look at some of the bigpicture features of scientific method.
 We will be focusing today on scientific
THEORIES. Next time, we will look in more
detail at scientific EXPERIMENTS.

SCIENCE
We’ll start by looking at the basic principle of
theory formation and testing in science – the
HYPOTHETICO-DEDUCTIVE METHOD.
 Then, we’ll look at some of the main features that
make scientific theories good or bad.

SCIENCE

We can talk about four main elements that go
into the practice of scientific research:
The WORLD
 The THEORY
 Theoretical PREDICTIONS
 Experimental DATA


The relationships that these elements have to
one another define the goals and methods of
science.
SCIENCE
The WORLD is what science is attempting to
characterize, describe, and explain.
 The objects, properties, events, processes, and so
on that are in the world make up the subject
matter of science – what science is about.

WORLD
SCIENCE
A scientific THEORY is a set of claims about the
world.
 This can include statements about facts, laws of
nature, etc.
 A good theory fits the world – it describes it
accurately.

WORLD
Fits
THEORY
SCIENCE
A theory in science only describes a small portion
of the world – maybe only a single phenomenon
or type of phenomenon.
 But it attempts to give a full explanation of that
phenomenon, leaving nothing ‘mysterious’ or
unexplained.

WORLD
Fits
THEORY
SCIENCE
Examples of theories in science: the theory of
evolution, the theory of gravity, the theory of
special relativity.
 Notice that each of these is more than just a
single statement or hypothesis – each is a
complicated set of laws and principles.

WORLD
Fits
THEORY
SCIENCE
Theories in science generate PREDICTIONS
about what will happen under certain specified
conditions.
 Predictions can be about the past, present, or
future.

WORLD
Fits
THEORY
Generates
PREDICTIO
N
SCIENCE

Examples of predictions: the theory of evolution
predicts the existence of transitional life-forms;
the theory of gravity predicts that if I drop
something it will fall.
WORLD
Fits
THEORY
Generates
PREDICTIO
N
SCIENCE

Predictions are the tools we use to test our
theories; if the prediction comes out true, this is
good for the theory. We do this through looking
at DATA.
WORLD
Generates
DATA
Fits
Confirms/
disconfirms
THEORY
Generates
PREDICTIO
N
SCIENCE
Data, of course, is generated by the world – we
usually elicit it by running EXPERIMENTS.
 Once we gather data, we can analyze it to see if it
either confirms or disconfirms our predictions.

WORLD
Generates
DATA
Fits
Confirms/
disconfirms
THEORY
Generates
PREDICTIO
N
SCIENCE
Let’s look at the process of testing a theory in
more detail. The process we use to test scientific
theories is called the HYPOTHETICODEDUCTIVE METHOD (Lau has the name
slightly wrong).
 It consists of four basic steps:

1) Identify hypothesis
 2) Generate predictions
 3) Gather data via experiment
 4) Determine whether hypothesis is confirmed or
disconfirmed.

SCIENCE
Let’s go over each step in detail.
 STEP ONE: Identify the hypothesis to be tested.

A hypothesis is a proposed explanation for something
we’ve observed.
 Generally, this hypothesis is a component of a theory
which we are trying to test. But it can also be a
proposed explanation that isn’t part of a bigger
theory.
 Example: One hypothesis that might arise from the
theory of evolution is the claim that animal
camouflage is an adaptation to a particular
environment that developed over many generations.

SCIENCE
Let’s go over each step in detail.
 STEP TWO: Generate predictions from the
hypothesis.

In this step, we want to come up with something we
can check in the world to see whether our hypothesis
is correct.
 Example: If our hypothesis is that animal camouflage
is an evolved adaptation to a particular environment,
we might predict that a creature with camouflage
that was put in a different environment would
eventually evolve new coloring.

SCIENCE
Let’s go over each step in detail.
 STEP THREE: Gather data via an experiment.

In this stage, we set up a situation under which our
prediction will be tested.
 Example: To test our prediction about animal
camouflage, we might introduce a certain kind of
short-lived animal – perhaps a moth – to a new,
different-colored environment and observe whether
its coloration changes after a large number of
generations.

SCIENCE
Let’s go over each step in detail.
 STEP FOUR: Determine whether the data
confirms or disconfirms the hypothesis.

If the prediction was right, the hypothesis is
confirmed; if it is wrong, the hypothesis is
disconfirmed.
 Sometimes this step will require analyzing the data
very closely – in particular, it’s important to try to
rule out the possibility that the data fit the prediction
only by coincidence.
 Example: Imagine we notice that most moths in the
50th generation were darker in color than moths of
the first generation, and they now match the local
colors. This would confirm our hypothesis.

SCIENCE
There are a few very important things to notice
about the hypothetico-deductive method.
 First, in order for the method to work, our
hypothesis must be able to generate predictions
that we can test.
 Since the hypothetico-deductive method forms
the foundation of science, this means that if a
hypothesis can’t doesn’t generate predictions we
can use the method to test, then it is not a
scientific hypothesis.

SCIENCE
Imagine someone tells you that they have the
following hypothesis: invisible, undetectable
ghosts are influencing world events.
 The problem with this hypothesis is that there is
no way to test it – the ghosts are undetectable,
and we can’t set up an experiment to show that
they are there. So the hypothesis cannot be said
to be a scientific one.

SCIENCE

Second: even though it contains ‘deductive’ in its
name, the hypothetico-deductive method is an
inductive method. If an experiment supports
your hypothesis, this provides good evidence for
the theory you are testing. But it does not
PROVE the theory.
SCIENCE
There are always alternate possibilities that
might explain why your experiment had the
results it did.
 Think of the moth case – maybe the moths are
getting darker because of some sort of disease
that is spreading through their population,
rather than due to adaptation.

SCIENCE
We said that an experiment can CONFIRM a
hypothesis, but this can be misleading.
 The word ‘confirm’ usually means something very
close to ‘prove’ or ‘make certain’. However, in
science, it just means that the probability that
the hypothesis is true has increased.

SCIENCE
Third: Disconfirming a theory does not mean it is
false.
 Again, ‘confirm’ and ‘disconfirm’ have special
meanings in science. When a theory is
disconfirmed, this only means that the
probability that it is true has decreased.
 It may still turn out true – particularly if there
was something wrong with the experiment!

SCIENCE
So now we have a basic idea of the fundamental
method of science.
 Next, let’s look at some of the features we look for
in a good scientific theory.

SCIENCE
Ideally, running experiments will give us some
idea about whether a theory is likely to be true or
false. But this isn’t the only thing we look for in
a good theory.
 In fact, sometimes multiple theories are
CONSISTENT with all the experimental data.
In this case, we may need to use other criteria to
decide which is the better theory.

SCIENCE

A good theory should:
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Have PREDICTIVE POWER
Explain the relevant phenomenon in terms of
underlying causal MECHANISMS
Be FRUITFUL
Be SIMPLE
Be COHERENT
Let’s look at each of these in turn.
SCIENCE
A good theory has a lot of PREDICTIVE POWER.
We’ve already mentioned that if a theory or
hypothesis doesn’t generate predictions, it is not
truly scientific at all.
 But the number and usefulness of the predictions
matter, too. A theory that only results in a few
predictions is hard to confirm/disconfirm. A
theory that generates a lot of predictions is more
desirable.

SCIENCE
A good theory also has EXPLANATORY POWER
– it’s capable of generating explanations of the
phenomena we’re interested in.
 A theory can gain explanatory power by
proposing CAUSAL MECHANISMS for the
phenomena we’re interested in.

SCIENCE

An example of a theory that doesn’t explain via
causal mechanisms:


Caffeine cures headaches.
This theory is testable and generates predictions
(like if I drink coffee, my headache will go away),
but it does not explain the relationship – it
doesn’t say why caffeine causes headaches to go
away.
SCIENCE

A similar theory that DOES explain via causal
mechanisms:


Caffeine causes the blood vessels in your brain to
contract, decreasing blood flow and therefore
pressure in the brain. This can help cure headaches.
This theory will make similar predictions to the
first, but it is much more explanatory. It tells us
WHY caffeine helps – it explains the mechanism.
SCIENCE
Another desirable feature in a theory is
FRUITFULNESS. This feature has to do with
how many applications the theory has outside its
direct area.
 Can it lead us to new predictions, or new
theories? Can it help make connections between
theories we already have?

SCIENCE
An example: The theory of evolution is a very
fruitful theory. It explains how complex animals
came to be, but it also helps explain things
outside its direct area.
 For example, the theory of evolution can explain
why it is so difficult to make a vaccine for colds –
cold viruses mutate and evolve very quickly.
 It also explains why antibiotics become less
effective over time.

SCIENCE
Another feature we can use to decide between
two theories is SIMPLICITY.
 Simplicity isn’t as important as predictive
power/accuracy, or explanatory power. But it can
help decide between two theories that both seem
to be very accurate and powerful.

SCIENCE

Example: “Caffeine reduces headaches by
constricting blood vessels” is a simpler theory
than “Caffeine reduces headaches by triggering
the release a not-yet-discovered chemical which
acts as a painkiller, and has the side effect of
constricting blood vessels”.
SCIENCE
Both theories might be consistent with the data;
but, the second introduces a new element – the
unknown chemical. This isn’t strictly needed in
order to explain why caffeine helps with
headaches – there is no reason to assume it
exists.
 All the ‘undiscovered chemical’ hypothesis does is
complicate the theory.

SCIENCE
Finally, a good theory should have the feature of
COHERENCE. This means two things – first,
the theory shouldn’t internally contradict itself.
It is very bad if a theory generates conflicting
predictions, for example.
 But it also means that the theory should fit well
with our other well-established scientific
theories.

SCIENCE
It would be a very bad thing if my new theory of
how black holes work contradicted the
predictions of the theory of gravity.
 Of course, sometimes good theories contradict
well-established theories – the theory that the
earth goes around the sun is an example.
 But most of the time, a theory that contradicts
current scientific understanding is likely to be
wrong.

NEXT TIME
We’ll finish with an inclass exercise today.
 The third homework is due in one week – it will
be posted on Moodle later tonight.
 Next time, we’ll talk about the details of
designing a good experiment, and of interpreting
experimental data.
