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Copyright © 2010 Pearson Education, Inc.
Chapter 1
Introduction to the Scientific Method Can Science Cure the Common Cold?
Copyright © 2010 Pearson Education, Inc.
1.1 The Process of Science
Science is not a giant collection of facts to be memorized.
Process, using the scientific method: Observing Proposing ideas Testing Discarding those ideas that fail
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1.1 The Process of Science
The Nature of Hypotheses Hypothesis: proposed explanation Testable and potentially falsifiable Where do hypotheses come from?
Copyright © 2010 Pearson Education, Inc. Figure 1.1
1.1 The Process of Science
The Nature of Hypotheses Both logical and creative influences are used
OBSERVATION
Imagination
IntuitionChance Logic
Experience
Previous scientificresults
Scientific theory
HYPOTHESIS
QUESTION
Copyright © 2010 Pearson Education, Inc.
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
The Logic of Hypothesis Tests Example: consuming vitamin C decreases
the risk of catching a cold Inductive reasoning: combining a series of
specific observations into a generalization
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1.1 The Process of Science
The Logic of Hypothesis Tests To test, make a prediction using deductive
reasoning. Involves using general principle to predict an
expected observation “if…then” statement
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1.1 The Process of Science
The Logic of Hypothesis Tests
The process looks something like this:
Figure 1.3
Hypothesis(that is testable and fasifiable)
Make prediction
Consuming vitamin C reduces the risk of catching a cold.
If vitamin C decreases the riskof catching a cold, then peoplewho 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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1.1 The Process of Science
Figure 1.3 (continued)
If people who take vitamin C suffer fewercolds 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
Consider alternative hypotheses
Copyright © 2010 Pearson Education, Inc.
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.
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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.
Figure 1.4
(b) How the virus causes a cold(a) Cold–causing virus
Virus introduces its genetic material into a host cell.
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).
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.
Protein shell
Genetic material and proteins
Virus
Host cell
Viruscopies
Releasedviruscopies
Immune system cells
Nasalpassages
Throat
Mucus
1
2
3
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1.2 Hypothesis Testing
The Experimental Method Experiments are contrived situations. Variables: factors that can change in value
under different conditions Independent variables can be manipulated by
the scientist Dependent variables cannot be changed by
the researcher
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1.2 Hypothesis Testing
Controlled Experiments Controlled experiment: tests the effect of a
single variable 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
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.
Figure 1.7
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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 blinding: 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.
Figure 1.10
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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
Figure 1.11
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1.3 Understanding Statistics
Overview: What Statistical Tests Can Tell Us We can extend the results from small
samples to an entire population. Difference between two samples: real or due
to chance?
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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 Confidence interval: the range of values
from a sample that has a 95% probability of containing the true population mean (average).
Much population variation = large confidence interval
Small population variation = small confidence interval
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1.3 Understanding Statistics
Factors that Influence Statistical Significance
Sample size The true difference between populations Bigger is better: more likely to detect
differences
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1.3 Understanding Statistics
Factors that Influence Statistical Significance
Figure 1.15
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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
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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1.5 Is There a Cure for the Common Cold?
No, but prevention methods are known. Wash your hands! No effect on cold susceptibility:
Vitamin C Exposure to cold temperatures Exercise
No vaccine for the common cold
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