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Lecture2-note - Review of lecture 1 Producing data:...

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1 Producing data: experiments BPS chapter 9 © 2006 W. H. Freeman and Company Review of lecture 1 Producing data: sampling ± Individuals and variables ± Explanatory, response and lurking variables ± Population versus sample ± Observation versus experiment ± How to sample badly ± Simple random samples ± Other sampling designs ± Caution about sample surveys ± Learning about populations from samples (inference) Individuals and variables Individuals are the objects described by a set of data. Individuals may be people, but they may also be animals or things. ± Example: Freshmen, 6-week-old babies, golden retrievers, fields of corn, cells A variable is any characteristic of an individual. A variable can take different values for different individuals. ± Example: Age, height, blood pressure, ethnicity, leaf length, first language Explanatory, response and lurking variables A response variable measures or records an outcome of a study. An explanatory variable explains changes in the response variable. A lurking variable is a variable not included in the study that may influence the interpretation of relationships between explanatory and response variables. A lurking variable can falsely suggest an explanation or a cause. Population versus sample ± Sample : The part of the population we actually examine and for which we do have data How well the sample represents the population depends on the sample design. ± A s tatistic is a number describing a characteristic of a s ample. ± Population: The entire group of individuals in which we are interested but can’t usually assess directly Example: All humans, all working-age people in California, all crickets ± A p arameter is a number describing a characteristic of the p opulation. Observation versus experiment Observational study: Record data on individuals without attempting to influence the responses. We typically cannot prove anything this way. Example: Based on observations you make in nature, you suspect that female crickets choose their mates on the basis of their health. Æ Observe health of male crickets that mated. Experimental study: Deliberately impose a treatment on individuals and record their responses. Influential factors can be controlled. Example: Deliberately infect some males with intestinal parasites and see whether females tend to choose healthy rather than ill males.
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2 Confounding Two variables (explanatory variables or lurking variables) are confounded when their effects on a response variable cannot be distinguished from each other. Observational studies of the effect of one variable on another often fail because the explanatory variable is confounded with lurking variables. studying intelligence Good grade on test CAUSE? Confounding?
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This note was uploaded on 04/26/2008 for the course BIOL 7 taught by Professor Luo during the Fall '07 term at UC Irvine.

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Lecture2-note - Review of lecture 1 Producing data:...

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