Chapter_10_1 - Chapter 10: Comparing Two Groups Design and...

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Chapter 10: Comparing Two Groups Read: Chapters 10 and 11 Design and Power { Java applets for power and sample size { This software is intended to be useful in planning statistical studies. { N.F.L. Data Reinforces Dementia Links { Study of Retired Professional Football Players { N.F.L.’s Dementia Study Has Flaws, Experts Say The History of Experimentation Experimentation characterizes modern science. Galileo (1564-1642) reportedly dropped balls of various masses from the Leaning Tower of Pisa. Assuming the story of Galileo’s Pisa experiment is true: How many balls did he drop? How many times did he repeat the comparison? What were his independent and dependent variables? How did he measure the time to impact? We don’t know the answers to these questions… Take Home Message: Experimental design was haphazard prior to the 1920’s. Ronald Aylmer Fisher (1890-1962) { Considered by some scientists to be the father of modern statistics. { Poor eyesight; did a lot of math in his head without paper or pencil. { In 1919, he began working as a statistician at the Rothamsted Agricultural Experiment Station in the United Kingdom. { Published many papers and wrote several books on experimental design and evolution. At Rothamsted, Fisher recognized problems with some of the agricultural experiments Fisher’s Solution: Replicate and randomize to spread variation evenly among treatments. Same field, same treatment, but plant performance is uneven. .. Thick Growth Thin Growth Lessons Learned at Rothamsted Experiments at Rothamsted prior to Fisher generally involved two fields (containing hundreds of plants), each receiving a treatment. Example: two levels of nitrogen (N) fertilizer Problem: So much variability exists within a field itself that it is difficult or impossible to tease out the effect the treatment. Field with High N Field with Low N
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Fisher’s Solution at Rothamsted z Old Problematic Design : One large field receiving high nitrogen (N), one large field receiving low nitrogen (N). z New Improved Design : Many small plots, randomly receiving high N or low N; plots can also be blocked to help tease out the variation due to location and local conditions. Examples of Correct & Incorrect Ways to Randomize Treatments Correct Ways: { Use random numbers Incorrect Ways: { Haphazardly decide which experimental units should receive which treatments. (Problem: too tempting for experimenter to bias.) { Use a net to grab the goldfish in an ecology study. (Problem: might pick just the easiest to catch, sickly animals.) { Alternate treatments (every other one). (Problem: that’s systematic, not random; who knows what other factors vary in the same systematic way.) { Assign people to drug study on the basis of their last name. (Problem: could be related to a person’s ancestry.) Fisher, Randomization, Replication & Blocking { No replication (or pseudoreplication) (Rothamsted, pre-Fisher) : { Replicated with complete randomization : { Replicated, randomized and blocked design : Field with High N Field with Low N Field broken up into smaller plots Plots are blocked by location or other condition; treatments
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This note was uploaded on 01/09/2010 for the course ILRST 2100 at Cornell University (Engineering School).

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Chapter_10_1 - Chapter 10: Comparing Two Groups Design and...

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