12_exprimnt_dsg1

# 12_exprimnt_dsg1 - Goal of experiments (and thus...

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Experimental Design, I 9.07 3/18/2004 Goal of experiments (and thus experimental design) minimum Determine whether a relationship is likely to exist between One or more independent variables (factors) and a dependent variable, or Two or more dependent variables Minimize the possibility that the results you get might be due to a hidden confounding factor Maximize the power of your test for this relationship, while keeping the probability of a Type I error to a Quantify your uncertainty in the results Wide range of applicability of the results Minimizing confounding factors Power fact exists. α replication ) 1 –m 2 ), or decreasing the irrelevant variability . Confounding = a difference between the “treatment” and comparison groups, other than the “treatment”, which affects the responses under study (the dependent variables). From homework: does dressing well cause you to do better on the SAT? Confounding factor = income. We’ll talk about experimental designs that are better or worse as far as confounding factors. Probability of detecting a relationship when one in Increasing power: Increase (tradeoff between Type I & Type II errors) Increase n ( Increase the “signal-to-noise ratio,” i.e. if possible, increase the size of the effect by either increasing the raw effect size (m Do a better statistical test. 1

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j about the variability in the responses, and thus can’t to conditions. results takes on wide range of values, think twice factors might interact ( Quantifying uncertainty Use replication If one sub ect does a task only once, you have no idea quantify uncertainty Use a proper form of randomization Our models make strong assumptions about, e.g. how subjects were chosen from the population and assigned If these assumptions are not correct, we can’t accurately quantify our uncertainty. Wide range of applicability of the If in the real world the independent variable about only testing a small range If there are a number of factors that might affect the results, understand how those factorial designs) Minimizing the possibility of control/comparison group Controlled experiments vs. observational studies j smoking vs. not) or by chance (exposure to radiation leak or not) confounding factors Much of experimental design is aimed, at least in part, at this problem Observational studies vs. controlled experiments Contemporaneous vs. historical controls Other issues with choosing the proper treatment and Use of placebos Double-blind experiments The Hawthorne effect We’ll talk about these design issues, as well as about Simpson’s Paradox, which is related •I n a controlled experiment , the experimenter assigns individuals to a group and decides upon the value of the independent variable (the
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## This note was uploaded on 11/11/2011 for the course BIO 9.07 taught by Professor Ruthrosenholtz during the Spring '04 term at MIT.

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12_exprimnt_dsg1 - Goal of experiments (and thus...

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