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United States Department of Agriculture

Agricultural Research Service


Location: Sustainable Biofuels and Co-products Research

Title: On the proper use of tools)

item Haas, Michael

Submitted to: European Journal of Lipid Science and Technology
Publication Type: Other
Publication Acceptance Date: 11/29/2010
Publication Date: 12/9/2010
Citation: Haas, M.J. 2010. On the proper use of tools. European Journal of Lipid Science and Technology. 112(12):1287-1289.

Interpretive Summary:

Technical Abstract: In the ‘Odds and Ends’ drawer of my desk is an old multi-bladed pocket knife, made of high quality steel. Although its two smallest blades are intact and sharp, its larger main blade is snapped off, a stub compared to its original length. This stub is a testament to the fact that even very good knives make very poor prybars. Similarly, in my gun cupboard sits a shotgun, many of whose screws have badly chipped and rounded slots for the corresponding screwdriver. These are a permanent testimony to the day that, as a much younger man, I learned that most screwdrivers and their companion screws have tapered blades and correspondingly tapered slots, but that firearms screws and screwdrivers have parallel faces. Regular screwdrivers therefore do not properly fit the screws in firearms, and when used in this improper application can slip out and destroy the screw head. These two examples illustrate the fact that a good tool used inappropriately is a recipe for disaster. In a Letter to the Editor in this issue, Dr. Albert Dijkstra essentially observes that the same goes for the tools we use in our research. He makes this observation in regard to response surface methodology (RSM), which, to paraphrase Wikipedia, is a statistics-based experimental method that explores the relationships between multiple experimental variables and one or more response variables. Response Surface Methodology involves the use of a sequence of designed experiments to identify the conditions that will give an optimal response. The technique has become popular in some types of research because it can produce a maximum amount of data from a relatively small number of reactions, and can yield powerful predictive results. In that it allows identification of interactions between multiple variables in an experimental design, RSM offers a power that is lacking in the more conventional approach of changing one variable at a time. An experienced scientist, Dr. Dijkstra has spent a long and productive career conducting research and managing research teams, projects and entire research centers. He is very good at what he does, and he is widely regarded as a wise and accomplished man. In his letter he presents a multi-faceted commentary on the use, or rather the misuses, of statistically designed experimental planning and the consequent application of response surface methodology to the results. I have a high regard for the author of the Letter, a skilled and wise researcher with a multitude of accomplishments in science and technology. Also, I agree with several of the cautions voiced in his Letter. But, as in looking at a glass with water in it and seeing it as either half empty or half full, I see some of the points raised in the Letter in a somewhat different light than does the author. After reading the Letter, please consider the following comments, whose numbers correspond to the similarly numbered items there: 1. Dubious significance: Here the author of the Letter observes that by addition of extra terms to a data fitting equation one can reduce the apparent standard error of the fit to the data. It is correctly noted that to do so without increasing the number of experimental determinations as well is ill advised. Perhaps RSM has become too easy to apply, and thus too easy to apply incorrectly, such as in without appreciating this fact. Some contemporary RSM programs will help recognize such a situation as this, reporting an ‘adjusted’ R2 value to alert the researcher to the presence of an excessive number of terms in the equation of fit. The Letter’s admonishment to conduct multiple repeat determinations in order to determine the standard error of the measurements is wise advice, best heeded by all who implement RSM. Also, the observation is well made that just because your computer will print results out to seven digits does not

Last Modified: 8/24/2016
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