Download Uncertainty

Physics 270 – Experimental Physics
“The Scientific Method”
Science as a Collection of Facts
Fact 1
Fact 2
Fact 3
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One possible definition: activities aimed at
understanding the natural world
Scientists have shared values and perspectives that
characterize a scientific approach to understanding
nature: a demand for naturalistic explanations
supported by empirical evidence that are testable
against the natural world.
Other shared elements include observations, rational
argument, inference, skepticism, peer review and
reproducibility of work.
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Observations of phenomena
Experiments
Empirical formulas
Simplify the phenomena
Models
by creating a model system
on which calculations
Laws / Theory
Let’s do some
experiments!
can be carried out to
study the phenomena.
Develop multiple approaches since you
aren’t sure which one will work.
Start
Goal
“This is not a pipe”
Painting by Rene Magritte
In science results are presented using precise
(though technical) arguments,
…with…
 testable consequences
 falsifiability
 reproducibility
Experimental Verification
And Reproducibility
“Truth” in science
Descriptions of some aspect of nature
in terms of a model. Any view of the natural world
that a scientist devises is just a model loaded with
assumptions and approximations of that world.
Models, in general, have limited applicability.
As data and technology improve, models are replaced
by others which explain a larger range of phenomena.
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Theory – the best available description of
nature – as close to “truth” as we get.
Theories are validated by experiments.
◦ There is no “truth-meter” in science.
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Experiments expose the limitations or
incorrectness of theories.
Something may only be known if it is proven
to be true.
Beliefs may be true or false.
Rationality is the best test of truth.
Our senses can easily be fooled!
Reductionism versus Wholism
Reduction
Reduce a complicated problem into its
constituents and aims to understand
that complex problem through the study
of its components
Wholism
a phenomena must be viewed as a whole
in order to understand its structure
Reductionist Example: The
Structure of Proteins
Proteins consist of amino acids.
These are assembled into
ribosomes.
The order of assembly is
determined by RNA after it is
copied from DNA.
DNA consists of 4 units called
nucleotides.
The structure of proteins is very
complicated, but here the
problem has been reduced to
the assemblage of simpler
building blocks.
Holistic Example: An ant hill
Complex physical, chemical,
and biological structure built
and sustained by millions of
ants.
Cannot be understood by
braking the ants into tiny parts.
Its essence is in the complexity
of the whole.
Deduction – logical development of
the consequences of an explanation
starts with theoretical model
⇒ testable prediction
⇒ observations under specific
conditions
⇒ confirmation or rejection of
the prediction and/or the
model
• Enrico Fermi proposed the existence
of the neutrino in 1930 because the
observed decay products from beta
decay seemed to violate mass and
energy conservation.
• In 1956, Cowen and co-workers
detected its existence.
Deduction versus Induction
Induction – generalization of
observed patterns
starts with observations
⇒observed patterns
⇒development of model
⇒testable predictions
⇒competition of models
⇒theory
•
John Snow in 1854 observed that patients who had
contracted cholera had been drinking water from a
particular pump in London.
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He suspected that the cholera was spread by
contaminated water.
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Led to Louis Pasteur’s formulation of germ theory in
1857.
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Bacteria and viruses were later confirmed by direct
observation, establishing their connection to disease.
http://espanol.video.yahoo.com/watch/327162/2140779
Fallacies
Circular Reasoning – Begging the
question
 Appeal to emotion
 Argument from authority
 Sweeping Generalization
 Irrelevant Conclusion
 Denying the antecedent
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For a given measureable parameter, there
exists a true value of that parameter for a set
of circumstances at a given time.
We do not know what it is, nor do we have
any independent means of knowing it.
Precision versus Accuracy
Probabilistic versus Deterministic Models