2010%20Module%20II_revised-1

2010%20Module%20II_revised-1 - BIOL 206 - 2010 Module II -...

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BIOL 206 - 2010 Module II - Sampling & Exploratory Data Analysis II. 1 MODULE II GATHERING DATA, EXPLORING ITS STRUCTURE, AND TESTING HYPOTHESES Instructor Professor Martin Lechowicz Question: Does proximity to a trail affect the growth of ash trees on Mont Royal? Objectives ± To understand the purpose and nature of sampling ± To appreciate the difference between continuous and categorical data ± To learn techniques for exploring the structure of data using statistical software ± To learn to interpret basic descriptive statistics: o Parametric: mean, standard deviation; standard error of the mean; confidence intervals o Nonparametric (distribution-free): median, quantiles ± To understand the logic of statistical comparisons between groups o Confidence intervals for continuous data o Chi-squared tests for categorical data. ± To interpret statistical analyses in a biologically meaningful manner Work required ± Before lab , prepare a flow chart of tasks in the lab for this module! ± Walk up to Mount Royal Park to sample tree saplings ± Return to lab to enter data into Excel spreadsheets ± Working with other students, undertake a preliminary data analysis ± Discuss the statistical methods and the structure of your data ± At home , working independently, undertake additional analyses of the class data Bring: ± Wear clothes and shoes suitable for working off-trail on slopes in the woods, rain or shine! ± Bring insect repellant, and carry your Epipen if you are severely allergic to bee or wasp stings. Procedures ± Meet your TA in the lab at the regular time – be on time! ± Go to Mont Royal with your group to collect data ± Return to lab to enter and explore your data ± Pick up homework assignment at the end of lab METHOD OF EVALUATION Take-home Assignment -- due 2 weeks after data is made available 60% Flow chart, performance in field sampling, participation in group discussions 40%
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BIOL 206 - 2010 Module II - Sampling & Exploratory Data Analysis II. 2 DATA IN SCIENCE Data, counts or measurements that describe phenomena, are the foundation of science. We often use data to detect patterns in nature that suggest hypotheses about the processes underlying those patterns. Hypotheses give us the basis to make predictions that may be more or less quantitative, and that with additional data can be used to test their validity by comparing predicted and observed values. Hypotheses and predictions can also arise in theory that is derived in the abstract using mathematical analyses that build on assumptions or known principles, but in the end only data can validate the assumptions of the theory or test its predictions. Without good data, we cannot do science. So what are the characteristics of good data? Good data have to be relevant to the problem at hand, have to be accurate and have to be unbiased. These three criteria often are intertwined.
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2010%20Module%20II_revised-1 - BIOL 206 - 2010 Module II -...

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