copyright © 2010 pearson education, inc. biology 103 section 2 dr. brent palmer syllabus ...

Copyright © 2010 Pearson Education, Inc.

Biology 103 Section 2 Dr. Brent Palmer Syllabus Schedule

Two Websites

1. UK Blackboard

elearning.uky.edu

2. Mastering Biology

www.masteringbiology.com


WHY YOU SHOULD CARE ABOUT BIOLOGY?


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



Destruction of tropical rain forests Hundreds of thousands of acres are cleared

every day 1-2 percent every year They will never grow back

Greenhouse effect is here Melting of glaciers and polar ice caps Crop failures, drought, famine



Loss of Biological diversity 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

may be gone Aesthetics and moral --> a barren planet



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 greenhouse effect? balance of world power?


Biology: Science for Life

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

Chapter 1


Chapter 1

Section 1.1 The Process of Science


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: Observing Proposing ideas - Hypotheses Testing the hypotheses Discarding those ideas that fail



The Nature of Hypotheses Hypothesis: proposed explanation for

observation Must be both testable & potentially falsifiable

Were to 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



Science, Technology, and Education

Who is conducting ‘science’?

Who is using technology?

Who is has more education?



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).



Science, Technology, and Education

Who is conducting ‘science’?

Who is using technology?

Who is has more education?



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



Scientific Theory Powerful, broad explanation of a large set of

observations Rests on many hypotheses that have been

tested Generates additional hypotheses



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


1.1 The Process of ScienceThe 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


1.1 The Process of ScienceThe 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!!



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 Uses an “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.

> it is possible to disprove a hypothesis, but never possible to prove a hypothesis


Chapter 1

End Section 1.1 The Process of Science


Chapter 1

Section 1.2 Hypothesis Testing


1.2 Hypothesis Testing

Does Vit C help 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



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 still be rejected in the future

>Facts in science is what we know and understand based on all currently available information, but may change when new information is available



Experiments The most powerful way to test hypotheses is

to do experiments



Example: 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 - 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



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.



Animation—Science as a Process: Arriving at Scientific Insights

PLAY



Example of Controlled Experiment Example: Echinacea tea experiment:

Hypothesis: drinking Echinacea tea relieves cold symptoms

Experimental group drinks Echinacea tea 5-6 times daily.

Control group drinks “placebo” or “sham” Echinacea tea.

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 blind experiment: 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?



Example of 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.

Correlational data is not as good as controlled experimental data Correlation does not demonstrate a ‘cause and

effect’ relationship.



Figure 1.11

The correlation might be due to other reasons.

Using Correlation to Test Hypotheses

Caution! Correlation does not imply causation.



Correlation Versus Experimental Data

Correlational data is not as good as a experimental data Correlation does not demonstate a ‘cause and

effect’ relationship.


Chapter 1

End Section 1.2 Hypothesis Testing


Chapter 1

Section 1.3 Understanding Statistics


1.3 Understanding Statistics

Overview: What Statistical Tests Can Tell Us Scientists use statistics to understand what the

results of their experiments mean. Statistics is a branch of mathematics that extends

the results from small samples to an entire population. It determines if the difference between two samples

are real or due to chance (i.e. sampling error)

Statistics measures: Sample size Variation with the sample



Overview: What Statistical Tests Can Tell Us Scientists use statistics to understand what

the results of their experiments mean. Statistics is a branch of mathematics that

extends the results from small samples to an entire population. It determines if the difference between two

samples are real or due to chance (i.e. sampling error)



The Problem of Sampling Error Sampling error = the effect of chance

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 sampling error and not experimental treatment



Example of 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?

Statistics will help!



Statistics and Sampling Error Statistics calculates the probability that a

result is simply due to sampling error.

Statistics measures: Sample size Variation with the sample

Statistically significant = an observed difference between experimental groups is probably not due to sampling error


1.3 Understanding Statistics Statistical Significance: a low probability that

experimental groups differ simply by chance

Only Exp 1 is “Statistically Significant”



Factors that Influence Statistical Significance

1.Sample size Bigger is better: more likely to detect

differences

2.Variance of the population Statistical significance is harder to find in highly

variable populations



How to interpret Statistical Significance 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



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


Chapter 1

End Section 1.3 Understanding Statistics


Chapter 1

Section 1.4 Evaluating Scientific Information


1.4 Evaluating Scientific Information

Where info comes from makes a difference!

Best resource = Primary Sources Researchers submit a paper about their

results to a professional journal (primary source). Peer review: evaluation of submitted papers by

other experts before publication

Not as reliable: Secondary sources books, news reports, the internet, and

advertisements



Science in the News Often from secondary sources

may be missing critical information or report the information incorrectly.

Consider the source of media reports.



Other poor sources of information Anecdotal evidence is based on one

person’s experience, not on experimental data. Example: a testimonial from a celebrity

Internet: Be careful with the internet since anyone can post information.

Paid Advertisements: Be very cautious about claims made in paid advertisements.



Understanding Scientific reports

Use your understanding of the process of science to evaluate science stories

Table 1.2 on page 24.


Chapter 1

End Section 1.4: Evaluating Scientific Information

Conclusion

Section 1.5: Is There a Cure for the Common Cold?


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!


Chapter 1

End Chapter 1

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