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

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

1.1 The Process of Science The Nature of Hypotheses Both logical and creative influences are used Chance Logic Intuition Experience Previous scientific results Imagination HYPOTHESIS Scientific theory OBSERVATION QUESTION Figure 1.1

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

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

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

(that is testable and fasifiable) 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

1.1 The Process of Science Figure 1.3 (continued) 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 Reject the the hypothesis hypothesis Conduct additional tests 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.

1.2 Hypothesis Testing The most powerful way to test hypotheses: do experiments

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 Throat 1 Virus Virus introduces its genetic material into a host cell. Protein shell Genetic material and proteins 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. Virus copies 3 Immune system cells 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

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

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.

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.

1.2 Hypothesis Testing Controlled Experiments People who received echinacea tea felt that it was 33% more effective at reducing symptoms. 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 blinding: the person administering the treatments also doesn’t know until after the experiment is over “gold standard” for experimentation

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?

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?

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

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

1.2 Hypothesis Testing 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?

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

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

1.3 Understanding Statistics Factors that Influence Statistical Significance Sample size The true difference between populations Bigger is better: more likely to detect differences

1.3 Understanding Statistics Factors that Influence Statistical Significance 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

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

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

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.

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.

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.

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