The document explores the evolution of the scientific method, emphasizing its historical foundations from ancient natural philosophers to modern figures like Francis Bacon and Karl Popper. It outlines the methodology of forming and testing hypotheses while highlighting the significance of controlled experiments, the challenges of bias and misinformation in science, and the importance of replicating studies. Finally, it discusses the ongoing debate about the status of fields like computer science as genuine sciences amidst public skepticism and the influence of external factors.

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The Scientific
Method
REM Lecture 2
Dr. Julie Greensmith

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Lecture Overview
The development of the scientific method
Inductive versus deductive practices
Theory, empiricism and certainty
Hypotheses
Designing experiments
Results can be positive or negative
Challenges for science

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Learning Outcomes
To understand what motivated the scientific
method, and the accepted processes
To understand what is a hypothesis
An appreciation of experimental design
An awareness of the challenges facing
science

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In the beginning
The study of the universe is really ancient
Astronomy, weather, seasons, medicine,
anatomy, botany, alchemy
‘Natural philosophers’ doubted that natural
phenomena were attributed to the actions of
the gods
Facilitated by the written word vs the oral
tradition
Evidence across many civilisations across the
world
Egyptian, Indian, Babylonian, Chinese

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Grecian and Roman Science
Thales and Aristotle
Greek natural philosopher Thales one of the first
recorded ‘scientific enquirers’
Socrates and Plato laid foundations for reasoning, logic
and enquiry - and wrote it down.
Aristotle arguably the most influential and his mode of
thought dominated dominated for more than 1000 years
Classification of animals, movement of planets, dreams,
psychology, geology, deduction, ethics, poetry

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The Dark Ages
Western Europe regressed
Only religious scholars
Much of the learning of the
ancients lost
Plato and Aristotle believed as
truth and fact
Superstition and the oral tradition

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The Enlightenment
The Age of Reason (1685-1815)
The Scientific Revolution
Went against the teaching of the
establishment and religion,
questioned the status quo
Rediscovery of classical
knowledge
As a result of persecution, secret
societies were formed

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Francis Bacon 🥓
Credited with the ‘Baconian Method’ (1620)
Introduced inductive methodology
Testing and refining hypotheses by observing, measuring, and
experimenting
Described in his opus magnum the Novum Organum - new
instrument
Argues that there are progressive stages of certainty which are
achieved through cycles of empirical observation and induction

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“Man, being the servant and interpreter of
Nature, can do and understand so much and so
much only as he has observed in fact or in thought
about the order of Nature: beyond this he neither
knows anything nor can do anything”
–Francis Bacon
“Aphorism I” Novum organum

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Sir Issac Newton 🍎
Controversial, argumentative, mathematician, philosopher,
alchemist, politician, economist, physicist and (perhaps)
personality disordered
“Making pies on Sunday night... punching my sister... threatening my
Father and Mother Smith to burn them and the house over them”
“Plato is my friend,Aristotle is my friend, but my best friend is
truth.”
Greatest contribution in the form in his own theory of calculus
the “Philosophiae Naturalis Principia Mathematica”
The three laws of motion and the first rigorous account of his
theory of universal gravitation

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Karl Popper
Influential modern science philosopher
The problem of demarcation - distinguishing scientific from
non-scientific theories
Development of verifyability criterion
“a statement is cognitively meaningful if and only if it is, in
principle, possible to verify”
Scientific theories have potential falsifiers, that they make
claims about the world that might be discovered to be false
Non-scientific theories do not have falsifiers, where there is
literally nothing which could potentially invalidate the theory

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The Scientific Method
Make an observation {from the universe or
from a body of existing literature}
Form a hypothesis
Conduct and experiment to test the hypothesis
Analyse data to accept or reject the hypothesis
Draw conclusions

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Basic Scientific
Methodology
Methods: tools,
strategies, techniques
used to conduct science
Methodology: the study
of how research is
performed

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Assumptions of Science
The Universe operates according to stable
underlying laws
e.g. the nature of gravitational forces
Our senses give us accurate information about
the world - empiricism
e.g. we are not living in some simulated reality
Simplicity is preferable - Occam’s Razor

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Reasoning 🐑 🐑 🐑
You see a field of white sheep
Inductive reasoning states:
“All the sheep I see in this field are white, therefore all
sheep are white”
Deductive reasoning states:
“The law of sheep states that all sheep are white and as
the animals in the field are sheep, therefore they are all
white”

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Inductive vs Deductive
Induction and deduction can both be used to
convince others of a truth or to falsify some
proposed idea
Induction infers the general case from a set of
specific observations, depending on the cases being
typical of a general form - science
Deduction is the act of reaching a conclusion by
showing that such a conclusion must follow from a
set of premises - logic

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Scientific Theories
When do scientific observations become theories?
The general usage for the word theory means potentially “I
have an intuition”
In science the word theory has a more specific meaning
E.g. theory of special relativity; theory of evolution; the big
bang theory; germ theory
When something has such overwhelming evidence that it is
accepted as (almost) a fact
In maths a ‘theorem’ (related word) also has special meaning

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Continuum of Certainty
Definitely False Definitely True

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Continuum of Certainty
Definitely False Definitely True
1 + 1 = 3
Fermats last theorem
sqrt(2) = a/b as integers
1+1=2
Pythagoras
Logical deductions

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Derive Hypothesis and
Assign Certainty
Science assigns probabilities to hypotheses which
are updated with new knowledge
Limiting the amount of assumptions
Can never be totally certain, but satisfied above a
threshold of evidence
A theory in science is a strongly supported
hypothesis which can be used to make predictions
and can also be falsifiable

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What is a hypothesis
A specific way of framing a research question
We looked at this on Friday - some groups
appeared to grasp this better than others…
Make a prediction about a phenomena which
is verifiable or falsifiable through testing
To eliminate bias by avoiding a predetermined
outcome

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Constructing a
Hypothesis
A hypothesis should be:
Testable using a methodology
Results should be verifiable or falsifiable
The results to prove or disprove the
hypothesis should be reproducible

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The Null Hypothesis
A test for the status quo
Changing a system/component/entity will
have no observable effect on the results
e.g administration of propranolol will
have no significant effect on the blood
pressure of the patient

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The Alternative
Hypothesis
Seeks to express what will happen if we predict
an effect on a system
E.g. administration of propranolol will
significantly reduce the blood pressure of the
patient
One sided vs Two sided tests
(we will cover this more in quantitative methods)

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Controlled experiments
An experiment in which everything is held constant
except for one variable
Involves the use of a control group
Eliminate as much uncertainty as possible within a
specific experiment
The more control you have in your experiments, the
easier it is to convince others of the significance of
your results

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Parameters and
Assumptions
Sometimes its not possible to control all
variables - especially true in psychological
experiments
Have to make assumptions - these must
be explicitly communicated when writing
about an experiment

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Experimental Design
Appropriate design depends on your hypothesis
E.g. running a clinical study into the effects of a new drug
Randomised controlled trials preferable
Groups receiving the experimental treatment are
compared with control groups receiving no treatment
(placebo) or a previously tested treatment (a positive-
control study)
How might this apply in Computer Science or HCI?

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Measurement issues
The act of observing a system changes the
behaviour of the system
e.g. if users know their web browsing
behaviour is being monitored, they will not
express their ‘natural’ behaviour
Measurement error: to be aware of the
amount of error in measurement to eliminate
bias from the results e.g. false positives

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Getting positive results
We all like to get positive results, to show that our
experiment was right
Tests to show that we are in ‘less doubt than before’,
towards understanding our chosen phenomena
Relies on having chosen the correct methods, performing
the correct number of experiments, and choosing the
most appropriate statistical tests
Rarely become a ‘fact’, just adds more evidence towards
a case being accepted as a theory

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Don’t sweat negative
results
Negative results is where you could not show any
significant difference to reject the null hypothesis
Often just as useful as it prevents others from
potentially pursuing a fruitless line of enquiry
Can be very disheartening as we all want that
‘eureka’ feeling
Publication bias towards positive results

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Replication studies are
very useful too
Must perform and document studies in a way in which
experiments can be reproduced
Replication studies aim to reproduce the results of
another to check, verify and to add weight to an
assertion - its how theories are made
Might seem ‘boring’ but are essential to verifying claims
Many instances of scientific misconduct where
replication studies have disproved ‘faked papers’ *
* an MSc project is great for replication studies

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Drawing conclusions
Conclusions should be specific to the evidence
presented in the research
Generalise and highlight the relationship to other related
results and research
Derive novel questions which have arisen from the
results of the experiments
Often overstated ‘deduction’ style claims made in papers
view such claims with healthy skepticism

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Big Question
Is ‘Computer Science’ a real
science?

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Challenges for science
Only 52% of AAAS scientists say that today generally
a good time for science (down from 79% in 2009)
e.g.A majority of the US public (57%) says that
genetically modified (GM) foods are generally unsafe
to eat, while 37% says such foods are safe; by contrast,
88% of AAAS scientists say GM foods are generally
safe.
Science biased by corporate funding and
underfunding by government agencies

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Battling information
overload
Both scientists and the public are
bombarded with both information and
misinformation
Exponential growth of scientific publications,
fuelled by online publishing, pay-for-
publication venues
Wikis, forums, whitepapers, videos, podcasts

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Knowing your sources
Get to know the prominent journals and books in a field -
the ‘old way’
Do you read and article and question the sources?
Readers only tend to question sources when they disagree
with a fact or opinion
News feeds edited for articles similar to your own views -
not to mention fake news and academic misconduct
Avoid blindly believing tertiary sources as ‘fact’, including
questioning your own beliefs

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Lies, Damn Lies and
Statistics
Beware of the presentation of ‘statistics’ without
questioning
Shampoo adverts in which “87% of women agree” - but
they only had 50 test participants
A DNA match for a murder being “One in a Million”
chance - 64.1 million people in UK. $ = 64 people (not
including visitors!)
Have a positive test for a rare disease (1 in 10000), but
the test is 99% accurate - which is the more likely?

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It's okay to not know about something, or to
say "I don't know" when put on the spot. That
was one of the things I found hardest to do
Not everything you read is correct or valid.
Critically assess other peoples research to
inform yours.
Study simple cases before going general. But
once you've done the general case don't forget
that most people need to see the simple cases
first to understand what is going on. I think
that some people are worried that their ideas
will seem trivial if they reveal the simple
cases that motivated and explain them. Not
true - simple is better!

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A lot of science is about using a structured
approach (can be qual or quant) to answer a
set question. It is very much the process, and
if you get a negative result but have done the
process right you've still learned something.
Research often starts with a curiosity and
bunch of questions in hand. If you can
answer some of those and end up having
even more questions than before, then you
have really done well - sustainable research
The point of research is to find stuff out and
tell people. Without doing the latter
*effectively* it's just playing.

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“Disappointment is when a beautiful hypothesis
is spoiled by an ugly fact”
–Sir Issac Newton