Why You Should Care About Biology

1. Biology 103 Section 2 • Dr. Brent Palmer • Syllabus • Schedule Two Websites 1. UK Blackboard elearning.uky.edu 2. Mastering Biology www.masteringbiology.com

2. WHY YOU SHOULD CARE ABOUT BIOLOGY?

3. 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

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

5. WHY YOU SHOULD CARE ABOUT BIOLOGY • 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

6. 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 greenhouse effect? • balance of world power?

7. Biology: Science for Life Introduction to the Scientific Method Can Science Cure the Common Cold? Chapter 1

8. Chapter 1 Section 1.1 The Process of Science

9. 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

10. 1.1 The Process of Science The Nature of Hypotheses • Hypothesis: proposed explanation for observation • Must be both testable & potentially falsifiable • Were to hypotheses come from?

11. 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 Scientific theory OBSERVATION QUESTION Figure 1.1

12. 1.1 The Process of Science Science, Technology, and Education • Who is conducting ‘science’? • Who is using technology? • Who is has more education?

13. 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).

14. 1.1 The Process of Science Science, Technology, and Education • Who is conducting ‘science’? • Who is using technology? • Who is has more education?

15. 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

16. 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

17. 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

18. 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

19. 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!!

20. 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 • Uses an “if…then” statement

21. 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. Figure 1.3

22. 1.1 The Process of Science If people who take vitamin C suffer the same number of colds or more than those who do not. . . If people who take vitamin C suffer fewer colds than those who do not. . . Conclude that prediction is true Conclude that prediction is false Do not reject Reject the the hypothesis hypothesis Consider alternative hypotheses Conduct additional tests Figure 1.3 (continued)

23. 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

24. Chapter 1 End Section 1.1 The Process of Science

25. Chapter 1 Section 1.2 Hypothesis Testing

26. 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

27. 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 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

28. 1.2 Hypothesis Testing Experiments • The most powerful way to test hypotheses is to do experiments

29. 1.2 Hypothesis Testing • Example: 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 Throat 1 Virus introduces its genetic material into a host cell. Virus 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. Protein shell Genetic material and proteins Virus copies Immune system cells 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 Mucus Figure 1.4

30. 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

31. 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.

32. 1.2 Hypothesis Testing PLAY Animation—Science as a Process: Arriving at Scientific Insights

33. 1.2 Hypothesis Testing 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.

34. 1.2 Hypothesis Testing Controlled Experiments • People who received echinacea tea felt that it was 33% more effective at reducing symptoms. Figure 1.7

35. 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

36. 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?

37. 1.2 Hypothesis Testing 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?

38. 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

39. 1.2 Hypothesis Testing 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.

40. 1.2 Hypothesis Testing Using Correlation to Test Hypotheses Caution! Correlation does not imply causation. • The correlation might be due to other reasons. Figure 1.11

41. 1.2 Hypothesis Testing Correlation Versus Experimental Data • Correlational data is not as good as a experimental data • Correlation does not demonstate a ‘cause and effect’ relationship.

42. Chapter 1 End Section 1.2 Hypothesis Testing

43. Chapter 1 Section 1.3 Understanding Statistics

44. 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

45. 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)

46. 1.3 Understanding Statistics 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

47. 1.3 Understanding Statistics 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!

48. 1.3 Understanding Statistics 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

49. 1.3 Understanding Statistics • Statistical Significance: a low probability that experimental groups differ simply by chance Only Exp 1 is “Statistically Significant”

50. 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