copyright © 2010 pearson education, inc. chapter 1 introduction to the scientific method can...

Copyright © 2010 Pearson Education, Inc.

Chapter 1

Introduction to the Scientific Method Can Science Cure the Common Cold?


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



The Nature of Hypotheses Hypothesis: proposed explanation Testable and potentially falsifiable Where do hypotheses come from?

Copyright © 2010 Pearson Education, Inc. Figure 1.1


The Nature of Hypotheses Both logical and creative influences are used

OBSERVATION

Imagination

IntuitionChance Logic

Experience

Previous scientificresults

Scientific theory

HYPOTHESIS

QUESTION



Scientific Theory Powerful, broad explanation of a large set of

observations Rests on many hypotheses that have been

tested Generates additional hypotheses



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



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



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.



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



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.


1.2 Hypothesis Testing

The most powerful way to test hypotheses: do experiments



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



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



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.



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.



Controlled Experiments People who received echinacea tea felt that it

was 33% more effective at reducing symptoms.

Figure 1.7



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



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?



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?



Using Correlation to Test Hypotheses Results of such a study: the number of colds

increases as stress levels increase.

Figure 1.10



Using Correlation to Test Hypotheses Caution! Correlation does not imply

causation. The correlation might be due to other

reasons.



Using Correlation to Test Hypotheses

Figure 1.11


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?



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



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



Factors that Influence Statistical Significance

Sample size The true difference between populations Bigger is better: more likely to detect

differences



Factors that Influence Statistical Significance

Figure 1.15



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


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



Information from Anecdotes Anecdotal evidence is based on one

person’s experience, not on experimental data.

Example: a testimonial from a celebrity



Science in the News Secondary sources may be missing critical

information or report the information incorrectly.

Consider the source of media reports.



Science in the News Be careful with the internet since anyone can

post information. Be very cautious about claims made in paid

advertisements.



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.


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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