Transcript Chapter 1

Chapter 1
Introduction to the Scientific Method
Can Science Cure the Common Cold?
PowerPoint lecture prepared by
Steve McCommas
Southern Illinois State University
Copyright © 2010 Pearson Education, Inc.
Biology 103
 Dr. Brent Palmer
 Syllabus
 Schedule
Three Websites
1. UK Blackboard
2. Class website
http://web.as.uky.edu/Biology/faculty/palmer
3. Mastering Biology
www.masteringbiology.com
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WHY YOU SHOULD CARE ABOUT BIOLOGY
Biology is about you!
 Cancer - 1 IN 4 people will get cancer
 Lung Cancer
 1/3 of U.S. smokes
 160,000/yr get lung cancer
 145,000 will die in 3 years (90%)
 Skin cancer –
 melanoma is the most deadly form of cancer!
 Breast cancer
 1 in 9 women will get
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WHY YOU SHOULD CARE ABOUT BIOLOGY
 Destruction of tropical rain forests
 Hundreds of thousands of acres are cleared
every day
 1-2 percent every year
 They will never grow back
 Green house effect is here
 Melting of glaciers and polar ice caps
 Crop failures, drought, famine
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WHY YOU SHOULD CARE ABOUT BIOLOGY
 Loss of Biological diversity
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100,000 species extinct in the next 20 yrs
nearly 1/4 of all species on earth
They are lost forever
genes are lost --> cure for cancer and AIDS
gone
 Aesthetics and moral --> a barren planet
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WHY YOU SHOULD CARE ABOUT BIOLOGY
 Overpopulation
 I remember 3 billion people on the planet, then
4, then 5, then 6 billion people
 Now 6,860,482,878 people in the world!
 population will double in 40 years
 10-14 billion people by 2050
 mostly in poor 3rd world countries
 can we continue to feed the world, especially
with the green house effect?
 balance of world power?
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1.1 The Process of Science
 Science is NOT a giant collection of facts to
be memorized.
 Science is a Process, using the scientific
method:
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Observing
Proposing ideas - Hypotheses
Testing the hypotheses
Discarding those ideas that fail
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1.1 The Process of Science
The Nature of Hypotheses
 Hypothesis: proposed explanation for
observation
 Testable and potentially falsifiable
 Were to hypotheses come from?
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1.1 The Process of Science
The Nature of Hypotheses
 Both logical and creative influences are used
Chance Logic
Experience
Intuition
Previous scientific
results
Imagination
HYPOTHESIS
OBSERVATION
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QUESTION
Scientific theory
Figure 1.1
1.1 The Process of Science
Science, Technology, and
Education
 Who is conducting
‘science’?
 Who is using technology?
 Who is has more
education?
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1.1 The Process of Science
Science versus Technology
 Science is a process that uses the
scientific method
 It may not require technology, depending
upon hypothesis
 Technology uses advanced
instrumentation
 But just by having instrumentation does not
mean it is being used to in science (i.e., not
testing hypothesis).
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1.1 The Process of Science
Science, Technology, and
Education
 Who is conducting
‘science’?
 Who is using technology?
 Who is has more
education?
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1.1 The Process of Science
Pseudoscience
 Pretends to be science
 Often starts with a conclusion and then
tries to find ‘proof’ for it
 Only accepts evidence that supports their
theory, and rejects evidence that does not
support it
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1.1 The Process of Science
Scientific Theory
 Powerful, broad explanation of a large set of
observations
 Rests on many hypotheses that have been
tested
 Generates additional hypotheses
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1.1 The Process of Science
Example: Germ Theory
 People used to think diseases were caused
by things like:
 bad air – so they would not go out at night, or
 bad blood – which they treated with blood
letting
 Louis Pasteur observed that
microorganisms caused milk to spoil
 Hypothesized the microorganisms caused
deseases too
 Robert Koch demonstrated that anthrax
bacteria caused the disease in mice
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1.1 The Process of Science
The Logic of Hypothesis Tests
 Inductive reasoning: combining a series of
specific observations into a generalization
 Fruits and Veg’s contain lots of Vit C
 People who eat lots of fruits and Veg’s are
generally healthier
 Vit C is an anti-inflammatory agent, which reduces
nose & throat irritation
 From these observations a hypothesis is formed:
 Consuming vitamin C decreases the risk of
catching a cold
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1.1 The Process of Science
The Logic of Hypothesis Tests
 Inductive reasoning
 EXAMPLE
 The sun rises in the east every morning
 It travels across the sky
 It sets in the west every morning
> Therefore, the sun rotates around the earth
 Just because a series of observations appear right
doesn’t mean they are.
> MUST BE TESTED!!
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1.1 The Process of Science
The Logic of Hypothesis Tests
 To test, make a prediction using deductive
reasoning.
 attempts to show that a conclusion necessarily
follows from a set of premises
 i.e. it predicts the outcome based of an
action, test or investigation
 Uses an “if…then” statement
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1.1 The Process of Science
The Logic of
Hypothesis Tests
 The process looks
something like this:
Hypothesis
(that is testable and fasifiable)
Consuming vitamin C reduces
the risk of catching a cold.
Make prediction
If vitamin C decreases the risk
of catching a cold, then people
who take vitamin C
supplements will experience
fewer colds than people
who do not.
Test prediction
Conduct experiment or survey
to compare number of colds
in people who do and do not
take vitamin C supplements.
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Figure 1.3
1.1 The Process of Science
If people who
take vitamin C
suffer fewer
colds than
those who do
not. . .
If people who
take vitamin C
suffer the
same number
of colds or
more than
those who do
not. . .
Conclude that
prediction is
true
Conclude that
prediction is
false
Do not reject
the hypothesis
Reject the
hypothesis
Conduct
additional
tests
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Consider
alternative
hypotheses
Figure 1.3 (continued)
1.1 The Process of Science
The Logic of Hypothesis Tests
 A hypothesis that fails our test is rejected
and considered disproven.
 A hypothesis that passes is supported, but
not proven.
 Why not? An alternative hypothesis might
be the real explanation.
> it is possible to disprove a hypothesis, but
never possible to prove a hypothesis
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1.2 Hypothesis Testing
Vit C helps prevent colds
 First proposed by Noble prize winning
chemist Linus Pauling in 1970
 Based on a few studies conducted between
1930s and 1970s
 Subsequently disproven by lots of more
thorough research
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1.2 Hypothesis Testing
 When is a hypothesis considered true?
 When one hypothesis has not been disproven
through repeated testing and
 all reasonable alternative hypothesis have been
eliminated.
 But may be rejected in the future
>Truth in science is what we know an understand
based on all currently available information, but
may be changed when new information is
available
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1.2 Hypothesis Testing
 The most powerful way to test hypotheses:
do experiments
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1.2 Hypothesis Testing
 Experiments support the hypothesis that the
common cold is caused by a virus.
(a) Cold–causing virus
(b) How the virus causes a cold
Nasal
passages
Host cell
1 Virus introduces its
genetic material into a
host cell.
Virus
Protein
shell
Throat
Genetic
material and
proteins
Virus
copies
2 The viral genetic material instructs the
host cell to make new copies of the
virus. Immune system cells target
infected host cells. Side effects are
increased mucus production and throat
irritation.
3 New copies of the virus are
released, killing host cell. These
copies can infect other cells in
the same person or cells in
another person (for example, if
transmitted by a sneeze).
Released
virus
copies
Immune
system cells
Mucus
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Figure 1.4
1.2 Hypothesis Testing
The Experimental Method - Terminology
 Experiments are carefully regulated
situations.
 Variables: factors that can change in value
under different conditions
 Independent variables can be manipulated by
the scientist
 Dependent variables change depending upon
the dependent variable
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1.2 Hypothesis Testing
Controlled Experiments
 Controlled experiment: tests the effect of a
single variable at a time
 Control: a subject who is not exposed to the
experimental treatment
 Differences can be attributed to the
experimental treatment.
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1.2 Hypothesis Testing
PLAY
Animation—Science as a Process: Arriving at
Scientific Insights
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1.2 Hypothesis Testing
Controlled Experiments
 Example: Echinacea tea experiment:
 Hypothesis: drinking Echinacea tea relieves
cold symptoms
 Experimental group drinks Echinacea tea 5-6
times daily.
 Control group drinks “sham” Echinacea tea
(placebo).
 Both groups rated the effectiveness of their
treatment on relieving cold symptoms.
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1.2 Hypothesis Testing
Controlled Experiments
 People who received echinacea tea felt that it
was 33% more effective at reducing
symptoms.
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Figure 1.7
1.2 Hypothesis Testing
Minimizing Bias in Experimental Design
 If human subjects know whether they have
received the real treatment or a placebo, they
may be biased.
 Blind experiment: subjects don’t know what
kind of treatment they have received
 Double blind experiment: the person
administering the treatments also doesn’t
know until after the experiment is over
 “gold standard” for experimentation
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1.2 Hypothesis Testing
Using Correlation to Test Hypotheses
 It is not always possible or ethical to
experiment on humans.
 Using existing data, is there a correlation
between variables?
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1.2 Hypothesis Testing
Using Correlation to Test Hypotheses
 Hypothesis: stress makes people more
susceptible to catching a cold
 Is there a correlation between stress and the
number of colds people have caught?
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1.2 Hypothesis Testing
Using Correlation to Test Hypotheses
 Results of such a study: the number of colds
increases as stress levels increase.
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Figure 1.10
1.2 Hypothesis Testing
Using Correlation to Test Hypotheses
 Caution! Correlation does not imply
causation.
 The correlation might be due to other
reasons.
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1.2 Hypothesis Testing
Using Correlation to Test Hypotheses
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Figure 1.11
1.3 Understanding Statistics
Overview: What Statistical Tests Can Tell Us
 Statistics extend the results from small
samples to an entire population.
 It determines if the difference between two
samples are real or due to chance
 Statistics measures:
 Sample size
 Variation with the sample
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1.3 Understanding Statistics
The Problem of Sampling Error
 Sampling error: the effect of chance
 We can calculate the probability that a result
is simply due to sampling error.
 Statistically significant: an observed
difference is probably not due to sampling
error
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1.3 Understanding Statistics
The Problem of Sampling Error
 Experimental and control groups (samples)
will never be identical because all living
organisms are unique
 Sometimes the observed difference between
groups is only due to sample error and not
experimental treatment
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1.3 Understanding Statistics
The Problem of
Sampling Error
 Effect of zinc
lozenges on length of
a cold
 Did zinc really
shorten colds?
 Or did those people
just get over the cold
faster anyway?
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1.3 Understanding Statistics
The Problem of Sampling Error
 Confidence interval: the range of values
from a sample that has a 95% probability of
containing the true population mean
(average).
 Large population variation = large confidence
interval
 Small population variation = small confidence
interval
 Standard Error = amount of variability in the
sample
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1.3 Understanding Statistics
 Confidence interval: the range of values
from a sample that has a 95% probability of
containing the true population mean
(average).
Only Exp 1 is
“Statistically
Significant”
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1.3 Understanding Statistics
Factors that Influence Statistical
Significance
 Sample size
 Bigger is better: more likely to detect
differences
 Variance of the population
 Statistical significance is harder to find in highly
variable populations
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1.3 Understanding Statistics
What Does Statistical Significance Really
Mean?
 Most scientists accept a 5% probability of
error (i.e. P<0.05)
 This means that the probability that the
experimental groups were different by sampling
error alone is 5%
 That means that 1 in 20 (5%) of statistically
significant research is really just a false
positive
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1.3 Understanding Statistics
Factors that Influence Statistical
Significance
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Figure 1.15
1.3 Understanding Statistics
What Statistical Tests Cannot Tell Us
 If an experiment was designed and carried
out properly
 Evaluate the probability of sampling error, not
observer error
 May not be of any biological significance
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1.4 Evaluating Scientific Information
Primary Sources
 Researchers can submit a paper about their
results to a professional journal (primary
source).
 Peer review: evaluation of submitted papers
by other experts
Secondary sources: books, news reports, the
internet, and advertisements
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1.4 Evaluating Scientific Information
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1.4 Evaluating Scientific Information
Information from Anecdotes
 Anecdotal evidence is based on one
person’s experience, not on experimental
data.
 Example: a testimonial from a celebrity
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1.4 Evaluating Scientific Information
Science in the News
 Secondary sources may be missing critical
information or report the information
incorrectly.
 Consider the source of media reports.
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1.4 Evaluating Scientific Information
Science in the News
 Be careful with the internet since anyone can
post information.
 Be very cautious about claims made in paid
advertisements.
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1.4 Evaluating Scientific Information
Understanding Science from Secondary
Sources
 Use your understanding of the process of
science to evaluate science stories.
 News media generally highlight only those
science stories that seem newsworthy.
 They are more likely to report a positive result
than a negative one.
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1.4 Evaluating Scientific Information
Checklist for
evaluating science
in the News
Table 1.2 on page 24.
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1.5 Is There a Cure for the Common Cold?
 No effect on cold susceptibility:
 Vitamin C
 Exposure to cold temperatures
 Exercise
 No vaccine for the common cold
 But prevention methods are
known.
 Wash your hands!
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