Abstract
After years of providing statistical advice to fellow faculty members and graduate students, I have come to realize that it is not necessarily the big issues, but lack of knowledge of basic data analysis principles that get my clients into trouble. My claim is that if researchers and students internalized two basic definitions they would not have any problems analyzing most of their experiments. The definitions of Experimental Unit (EU) as the smallest physical unit to which a treatment may be applied and Experimental Error (Exp. Err.) as the variation among EUs treated alike are the basis for successful data analysis of experiments. I follow a seven-step data analysis program for my graduate student and faculty clients: (1) Understanding the experiment; (2) Checking the data; (3) Getting a feel for the data; (4) Checking underlying assumptions; (5) Testing; (6) Estimating; and (7) Interpreting results. Clients who have adhered to the program generally have had fewer problems than clients who, for some reason or another, did not get on board of the program. I will also touch on the implications for teaching experimental design and data analysis to non-statistics majors.
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Recommended Citation
Santen, Edzard van
(2013).
"THOU SHALL NOT BRUSH YOUR TEETH WHILE EATING BREAKFAST: A 7- STEP PROGRAM FOR RESEARCHERS PREVIOUSLY HURT IN DATA ANALYSIS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1015
THOU SHALL NOT BRUSH YOUR TEETH WHILE EATING BREAKFAST: A 7- STEP PROGRAM FOR RESEARCHERS PREVIOUSLY HURT IN DATA ANALYSIS
After years of providing statistical advice to fellow faculty members and graduate students, I have come to realize that it is not necessarily the big issues, but lack of knowledge of basic data analysis principles that get my clients into trouble. My claim is that if researchers and students internalized two basic definitions they would not have any problems analyzing most of their experiments. The definitions of Experimental Unit (EU) as the smallest physical unit to which a treatment may be applied and Experimental Error (Exp. Err.) as the variation among EUs treated alike are the basis for successful data analysis of experiments. I follow a seven-step data analysis program for my graduate student and faculty clients: (1) Understanding the experiment; (2) Checking the data; (3) Getting a feel for the data; (4) Checking underlying assumptions; (5) Testing; (6) Estimating; and (7) Interpreting results. Clients who have adhered to the program generally have had fewer problems than clients who, for some reason or another, did not get on board of the program. I will also touch on the implications for teaching experimental design and data analysis to non-statistics majors.
