Natural Science
Testing Scientific Ideas
Testing hypotheses and theories is at the
core of the process of science. Any aspect
of the natural world could be explained in
many different ways.
It is the job of science to collect all those
plausible explanations and to use scientific
testing to filter through them, retaining
ideas that are supported by the evidence
and discarding the others.
test
In science, an observation or
experiment that could provide
evidence regarding the accuracy of a
scientific idea. Testing involves figuring
out what one would expect to observe
if an idea were correct and comparing
that expectation to what one actually
observes.
hypothesis
A proposed explanation for a fairly
narrow set of phenomena, usually
based on prior experience,
scientific background knowledge,
preliminary observations, and logic.
theory
In science, a broad, natural explanation for a
wide range of phenomena. Theories are concise,
coherent, systematic, predictive, and broadly
applicable, often integrating and generalizing
many hypotheses. Theories accepted by the
scientific community are generally strongly
supported by many different lines of evidencebut even theories may be modified or
overturned if warranted by new evidence and
perspectives.
science
Our knowledge of the natural world and
the process through which that knowledge
is built. The process of science relies on the
testing of ideas with evidence gathered
from the natural world. Science as a whole
cannot be precisely defined but can be
broadly described by a set of key
characteristics.
natural world
All the components of the physical
universe — atoms, plants, ecosystems,
people, societies, galaxies, etc., as well as
the natural forces at work on those things.
Elements of the natural world (as opposed
to the supernatural) can be investigated by
science.
evidence
Test results and/or observations that
may either help support or help refute
a scientific idea. In general, raw data
are considered evidence only once
they have been interpreted in a way
that reflects on the accuracy of a
scientific idea.
You can think of scientific testing as occurring in
two logical steps:
(1) if the idea is correct, what would we expect
to see, and
(2) does that expectation match what we
actually observe?
Ideas are supported when actual observations
(i.e., results) match expected observations and
are contradicted when they do not match.
expectation
In science, a potential outcome of a scientific
test that is arrived at by logically reasoning
about a particular scientific idea (i.e., what we
would logically expect to observe if a given
hypothesis or theory were true or false). The
expectations generated by an idea are
sometimes called its predictions. Observations
that match the expectations generated by an
idea are generally interpreted as supporting
evidence. Mismatches are generally interpreted
as contradictory evidence.
observe
To note, record, or attend to a result, occurrence,
or phenomenon. Though we typically think of
observations as having been made "with our
own eyes," in science, observations may be
made directly (by seeing, feeling, hearing,
tasting, or smelling) or indirectly using tools.
Testing ideas with evidence is at the
heart of the process of science.
Scientific testing involves figuring out
what we would expect to observe if an
idea were correct and comparing that
expectation to what we actually
observe.
Science neither proves nor
disproves. It accepts or rejects
ideas based on supporting and
refuting evidence, but may revise
those conclusions if there are
reasons such as new evidence or
perspectives
TESTING IDEAS ABOUT CHILDBED
FEVER
A doctor worked on a maternity ward
in the 1800s. In his ward, an unusually
high percentage of new mothers died
of what was then called childbed fever.
The doctor considered many possible
explanations for this high death rate.
In the late 1840's, Dr. Ignaz
Semmelweis was an assistant
doctor.
One thing that he took care of was
the delivery rooms. (rooms where
women give birth to babies)
17
Semmelweis found that the death
rate in a delivery room staffed by
medical students was up to three
times higher than in a second
delivery room staffed by
midwives.
18
In fact, women were terrified of the
room staffed by the medical
students.
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First clinic
Year
1841
1842
1843
1844
1845
1846
Second clinic
Births Deaths Rate (%)
Births Deaths Rate (%)
3,036
3,287
3,060
3,157
3,492
4,010
2,442
2,659
2,739
2,956
3,241
3,754
237
518
274
260
241
459
7.8
15.8
9.0
8.2
6.9
11.4
86
202
164
68
66
105
3.5
7.6
6.0
2.3
2.0
2.8
Puerperal fever mortality rates for the First and Second Clinic at the
Vienna General Hospital 1841–1846. The First Clinic has the
larger mortality rate.
Semmelweis observed that the
students were coming straight
from their lessons in the
autopsy room to the delivery
room.
(Autopsy – cutting up dead
bodies to find out how they
died)
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Two of the many ideas that he considered
were
(1) that the fever was caused by mothers
giving birth lying on their backs (as
opposed to on their sides) and
(2) that the fever was caused by doctors'
unclean hands (the doctors often
performed autopsies immediately before
examining women in labor)
He tested these ideas by
considering what expectations
each idea generated.
Please go to www.socrative.com
Student log in
Room number 7318a
Semmelweis considered that the fever
might be caused by mothers giving birth
lying on their backs (as opposed to on
their sides). What expectations could
this idea generate?
www.socrative.com
Semmelweis considered that the fever was
caused by doctors' unclean hands (the doctors
often performed autopsies immediately
before examining women in labor). What
expectations could this idea generate?
He tested these ideas by considering what
expectations each idea generated.
If it were true that childbed fever were caused
by giving birth on one's back, then changing
procedures so that women labored on their
sides should lead to lower rates of childbed fever.
The doctor tried changing the position of labor,
but the incidence of fever did not decrease; the
actual observations did not match the expected
results.
www.socrative.com
The doctor tried changing the position of labor,
but the incidence of fever did not decrease;
the actual observations did not match the
expected results. Do you think that the idea
was supported by the evidence?
If childbed fever were caused by doctors'
unclean hands, having doctors wash their
hands thoroughly with a strong disinfecting
agent before attending to women in labor
should lead to lower rates of childbed fever.
When the doctor tried this, rates of fever
fell; the actual observations matched the
expected results, supporting the second
explanation.
www.socrative.com
When doctors washed their hands thoroughly
with a strong disinfecting agent before
attending to women in labor , rates of fever
fell; the actual observations matched the
expected results.
DOES HAND WASHING WORK?
SEMMELWEIS - 1847
% Mortality
Month Births
April 312
Deaths
57
18.3
May
294
36
12.2
June
July
268
250
6
3
2.4
1.2
32
www.socrative.com
Describe what happens in the slide labeled
“Does Hand Washing Work?”
www.socrative.com
Can you develop different explanations for what
happens in the slide labeled “Does Hand
Washing Work?”
Here is some of his evidence
Puerperal fever monthly mortality rates for the First Clinic at Vienna
Maternity Institution 1841–1849. Rates drop markedly when Semmelweis
implemented chlorine hand washing mid-May 1847
In his 1861 book, Semmelweis presented evidence to demonstrate that the advent ofpathological anatomy in
Wien (Vienna) in 1823 (vertical line) was accompanied by the increased incidence of fatal childbed fever.
The second vertical line marks introduction of chlorine hand washing in 1847. Rates for the Dublin
maternity hospital, which had no pathological anatomy, are shown for comparison.
Over time doctors began to accept the
ideas of Semmelweis. Doctors and
nurses began to wash their hands
more often and more carefully, using
strong soaps and disinfectants.
Now the ideas are strongly accepted
and encouraged by major health
groups.
• For example here are some prezis about
Semmelweis.
http://prezi.com/explore/search/?csrfmiddlewar
etoken=d9fc3764ef3ab9971bf9a99dd257ff04
&search=Semmelweis#search=Semmelweis&r
eusable=false&page=1&users=less
HANDS ARE THE MAJOR SOURCE OF
PATHOGENS
–Hands are the
most common
vehicle to
transmit health
care-associated
pathogens
40
SO WHY ALL THE FUSS ABOUT
HAND HYGIENE?
• Most
common
mode of
transmission
of pathogens
is via hands!
41
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GLOBAL HAND WASHING DAY
Global Hand washing
Day is a campaign to
motivate and mobilize
millions around the
world to wash their
hands with soap. The
campaign is dedicated
to raising awareness of
hand washing with
soap as a key
approach to disease
prevention.
43
HAND WASHING A TRIBUTE
TO DR. IGNAZ SEMMELWEIS
44
Maybe look at memrise.com
Testing ideas with evidence is at
the heart of the process of
science.
Scientific testing involves figuring
out what we would expect to
observe if an idea were correct
and comparing that expectation
to what we actually observe.
Fair Tests
Which restaurant serves the best,
cheapest meals?
What is causing your runny nose?
Why won't your computer work?
If you want to answer questions like
these, you'll probably need to do some
testing.
To find the real answers to such
questions, you'll need to test
your ideas in a fair way.
The parts of a fair test are the same:
Comparing outcomes.
Controlling variables.
Avoiding bias.
Distinguishing chance from real
differences.
Comparing outcomes.
To be confident in test results, it's
generally important to have something
to compare them to.
In experiments, whatever you are
comparing your test results to is
sometimes called the control group or
control treatment.
control group
In scientific testing, a group of individuals or cases
matched to an experimental group and treated in
the same way as that group, but which is not
exposed to the experimental treatment or factor
that the experimental group is. Control groups are
especially important in medical studies in order to
separate placebo effects from outcomes of interest.
Control groups are sometimes also called control
treatments or simply controls. This can be confusing
since this use of the term is slightly different from
what we mean when talking about controlled
variables.
Controlling variables.
In most tests, we want to be confident in
the relationship between cause and effect.
To be able to make a strong statement
about cause and effect, you'll need to
control variables — that is, try to keep
everything about the test comparisons the
same, except for the variables you're
interested in.
control
In scientific testing, to keep a variable or
variables constant so that the impact of
another factor can be better understood.
Avoiding bias.
No matter how hard we try to be objective, bias
can effect our observations and judgments. In a
sense, bias occurs because it's very difficult to
"control" variables associated with human
judgments.
objective
Not influenced by biases, opinions, and/or
emotions. Scientists strive to be objective in
their reasoning about scientific issues.
bias
Any deviation of results or inferences from the truth, or
processes leading to such deviation.
Bias can result from several sources: one-sided or
systematic variations in measurement from the true
value (systematic error); flaws in study design;
deviation of inferences, interpretations, or analyses
based on flawed data or data collection; etc. There is
no sense of prejudice or subjectivity implied in the
assessment of bias under these conditions.
For example, when a researcher or patient knows what
treatment is being given. To avoid bias, a blinded study
may be done.
Distinguishing chance from real
differences.
All sorts of subtle things that you either
don't or cannot control can affect the
outcome of a test.
All of these random factors will affect the
outcome of the test — but in small ways.
So how do you know if the outcomes of a
test are due to random factors or to real
differences?
First, sample size is important. The larger
your sample size, the more likely it is that
these random factors will cancel each
other out and that real differences (if
they exist) can be detected statistically.
Secondly, statistics can be used to analyze
your raw data. The purpose of
conducting such statistical tests is to tell
you how likely it is that a difference in
rating like the one that you observed is
actually due to random factors.
sample
In science, to collect information from part of an
entity, with the aim of learning about the entity as a
whole (e.g., to collect information on a subset of
the members of a population or on cores of ice
from the Antarctic). The term sample size refers to
the number of repeated measurements made (e.g.,
the number of individuals surveyed or the number
of ice cores studied). All else being equal, the larger
the sample size, the more confident we can be that
our sample represents the entity as a whole and the
more subtle the difference between samples that
we'll be able to discriminate.
data
Information got from observations — usually
observations that are made in a standardized
way. The term data generally refers to raw data
— information that has not yet been
analyzed. Data (multiple pieces of information)
is the plural form of datum (a single piece of
information).
Here is a simple example of how
sample size can effect the results.
People in Europe often saw these birds.
They are swans.
64
People from Europe thought that
all swans were white.
This can be written as a
hypothesis.
If it is a swan,
then it will be white.
65
People from Europe thought that
all swans were white.
This can be written as a
hypothesis.
What hypothesis could you write?
66
When Europeans went to
Australia they saw these birds.
67
68
They had a hypothesis, and looked for
evidence, but their sample size wasn’t
large enough.
DETECTING THE
DIFFERENCES:
STATISTICS AND SAMPLE
SIZE
What is a "large" sample size?
It depends on how small a difference between
groups you want to be able to detect. If you are
interested in very tiny differences, you need a
very large sample size, and if you only care
about pretty big differences, you can get away
with a smaller sample size. The appropriate
sample size depends on the statistical tests you
want to run and the sorts of differences you
want to detect.
It is often impossible to make a test
perfectly fair, and each issue listed
above may be more or less important
for a particular test — but by
considering each of these factors in
how your test is designed, you can
maximize the amount of useful
information you get from the test.
Above, we gave an example of
testing in everyday life, but the
same set of considerations can be
applied to tests in more scientific
areas — and to tests that don't
involve experiments.
Summary
Designing a fair test of an idea — in
formal science or in everyday life
— means deciding what results
you'll be comparing, controlling
variables, avoiding bias, and
figuring out a way to distinguish
chance differences from
meaningful ones.
Controlled variables are those
factors that are kept constant
across a test, so that the effect of
another variable can be better
observed.
The larger the sample size a test
employs, the smaller the
difference that the test will be
able to detect.