[intro music]
[intro music]
Welcome back.
In this lecture, we look at one of the foundational
tools that describe the way a scientist thinks
and works when deriving new knowledge about
the natural world.
In one of the previous lectures, we described
science as "a method of investigating nature,
a way of knowing about nature, that discovers
reliable knowledge about it" where reliable
knowledge is knowledge that has a high probability
of being true because it has been tested,
verified, and justified by a rigorous, reliable
method.
In this lecture, we talk about this "reliable
method" scientists practice to find the truth
about the natural world.
The process is called the Scientific Method,
and it consists of the following steps.
First, we might begin by asking a question
or identifying a problem in nature.
The birth of these questions can come from
the observations of phenomena or some annoyance
that is begging to be resolved.
Many times questions can germinate by chance
and serendipity or simply be the imaginings
of a curious mind.
That being said, the questions need to be
answerable, and hopefully, verifiable by empirical
evidence.
Empirical evidence is evidence that is susceptible
to one's senses, but also evidence that can
be verified by others.
Okay, so now let's gather data!
We will collect and organize observations.
Sometimes this step is the first step and
results in the production of a question, so
don't blindly think this scientific method
works in a certain way!
These observations can be data produced by
others, data we obtain by researching and
reading the scientific literature or observations
and initial data we have gathered ourselves.
For larger data sets, it is extremely helpful
to organize the information in different ways,
and hopefully, we will see patterns or discover
correlations.
It's now a good time to generate an educated
guess or list of guesses answering the question.
This guess forms what we call our scientific
hypothesis.
The hypothesis needs to be consistent with
observations but also needs to be testable.
Scientists must be skeptics!!
They should be eager to put their hypothesis
under scrutiny.
The hypothesis also needs to be predictive.
But you might be saying, hey what about supernatural
explanations?
[Spooky sound effect]
Blaming things upon the ghost of Albert Einstein
or thinking up some other supernatural reason
as a possible source of the phenomenon happening
around us might sound plausible, but it is
not something that's easy to test!
So it makes sense that the next step in our
reliable method is to make some prediction
using the hypothesis and then test to see
if we were correct.
The common technique is to design a clever
experiment to prove or disprove the hypothesis.
This can take considerable training, imagination,
and inventiveness on the part of the scientist.
We also must realize that some problems are
just not amenable to direct experimentation
such as hypotheses about the origins of the
universe or ancient cataclysmic events that
happened over several millennia.
These concepts, as you can very well imagine,
would be very complicated to test directly.
Even so, these questions may be approached
by making predictions and by further observations
and reorganizing of the data in new and creative
ways, thus generating a test.
Now we ask a critical question.
Did the hypothesis pass or fail all our experimentation
and testing?
If it fails a single test, the hypothesis
must be rejected or modified to explain this
new result.
Once the hypothesis is modified, it is fed
back into the process, and we do the testing
step again.
If it passes all the tests and experiments
that we have been able to subject it to, we
accept the hypothesis, but we must accept
it tentatively.
Remember, scientists must be skeptics!
The hypothesis, at times, can never be said
to be perfect "truth" or fully "proved" because
it is hard to imagine that we have considered
all possible experimental conditions.
There are many examples in history of hypotheses
that have been abandoned as our instrumentation
has gotten more sophisticated.
Also, as we have gotten better at designing
clever experiments, the knowledge gained on
many occasions has forced us to change the
interpretations of the old data.
In truth, the Scientific Method does not follow
a linear path but can be messy and cyclic
with the hypothesis continually being tweaked
and tested.
Often, other scientists jump into the fray
making the process collaborative.
Past and present experiments are continually
analyzed to minimize bias, minimize systematic
error, which is human error that can be fixed,
and to build experiments that are more precise
which is the minimization of random error
which is unfixable error present in every
experiment.
Also, we want to continually minimize and
reject inadequate data, increasing the quality
of the qualitative and quantitative analysis.
Qualitative analysis is an analysis that doesn't
require numbers, and quantitative analysis
involves producing a number and a unit.
It becomes important that the work is made
available to other scientists in print or
scientific meetings so even more scientists
can join in the discussion and test the hypothesis.
It is for this reason why we put so much value
on published results, and why we are so sensitive
to scientific fraud.
Shady ethics undermine the scientific process,
causing huge misinterpretations of the hypothesis,
wasting precious resources, and producing
delays to our understanding of the science.
If the hypothesis continues to pass tests,
it becomes reliable knowledge.
Though the hypothesis has made it through
the scientific fire, it must continue to undergo
the gauntlet of more tests and experimentation.
Many times, the testing will germinate a new
hypothesis.
I want to emphasize again; the scientific
method is not a linear process but a dynamic
circular cycle that grows and generates more
knowledge.
So that's the Scientific Method.
There are a couple more categorizations of
knowledge we need to be made aware.
Sometimes information transcends the idea
of reliable knowledge and is so obviously
"true" that it becomes too "perverse or irrational
to deny it".
This is sometimes called a scientific fact
or law.
For example, it would be a complete waste
of time for us to continue to conduct experiments
where we drop a ball hoping to get a different
result for the gravitational pull of the earth!
At this point, we have accepted the general
law of gravity.
A "scientific theory is a unifying and self-consistent
explanation of fundamental natural processes
or phenomena that is totally constructed of
corroborated hypotheses".
They are integrated and comprehensive explanations
of natural phenomena that generate even more
hypotheses and testable predictions.
Some theories can become so large in scope
that they incorporate innumerable facts and
laws and huge amounts of tested hypotheses.
Examples of such large ideas are quantum theory
and evolutionary theory.
It makes you wonder.
Do we dare dream of a theory that can explain
"Everything?"
[echo on the word everything?]
As a scientist, the word theory doesn't carry
the connotations of doubt and mistrust that
we see in popular culture.
A scientist should be a skeptic about their
theories, but scientists also appreciate how
much testing and collaboration a scientific
theory has gone through even to be called
a theory.
A scientific theory is supposed to be far
from being an unguided guess or approximation.
Well, we are done, but we see, once again,
that science is an active, vigorous exercise.
Something we practice and continually work
at.
You will rarely see an obvious mark of the
scientific method being used, but it will
be present.
Almost every kernel of truth you will be given
in this series of lectures is a product of
its application, and a mastery of its use
is critical to producing future scientific
knowledge.
Well, gotta go now, look forward to seeing
you again soon!
[closing music]
[closing music]
