Statistics in the Design and Analysis of Physiology Experiments
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University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Faculty Papers and Publications in Animal Science Animal Science Department January 1965 STATISTICS IN THE DESIGN AND ANALYSIS OF PHYSIOLOGY EXPERIMENTS L. Dale Van Vleck University of Nebraska-Lincoln, [email protected] C. R. Henderson Cornell University Follow this and additional works at: https://digitalcommons.unl.edu/animalscifacpub Part of the Animal Sciences Commons Van Vleck, L. Dale and Henderson, C. R., "STATISTICS IN THE DESIGN AND ANALYSIS OF PHYSIOLOGY EXPERIMENTS" (1965). Faculty Papers and Publications in Animal Science. 322. https://digitalcommons.unl.edu/animalscifacpub/322 This Article is brought to you for free and open access by the Animal Science Department at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Faculty Papers and Publications in Animal Science by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Van Vleck, L. D. 1965. Statistics in the design and analysis of physiology experiments. Journal of Animal Science 24:559‑567. Abstract: Statistics should be considered as a tool by the animal experimenter in much the same way that a chemical analysis or a radiation counter is used as a tool. Too often statistics is considered as something magical which can restore order out of chaos and perhaps absolve the experimenter from mistakes in logic and procedure. It is true of course that the statistician through his knowledge of statistical procedures can sometimes help salvage some results from an otherwise hopelessly muddled experiment. One point must be made clear at the outset—the statistician is not a tool of the experimenter although statistics is such a tool. What then is the proper role of the statistician in biological experimentation? In this time of specialization, most professional men and women are highly trained in one aspect of one field of interest. The old cliche that continued education is simply “Learning more and more about less and less” is nevertheless cogently descriptive of our age. Copyright © 1965 American Society of Animal Science. Used by permission. STATISTICS IN THE DESIGN AND ANALYSIS OF PHYSIOLOGY EXPERIMENTS 1 L. D. VAN VLECK AND C. R. HENDERSON Cornell University, Ithaca, New York TATISTICS should be considered as a deeper than the survey courses biologists take S tool by the animal experimenter in much in the humanities or social sciences. An expert the same way that a chemical analysis or a knowledge is not required. In order to talk in- radiation counter is used as a tool. Too often telligently to the statistician, however, at least statistics is considered as something magical a knowledge of the rudiments of statistics is which can restore order out of chaos and per- necessary. haps absolve the experimenter from mistakes A reasonable statistical requirement for re- in logic and procedure. It is true of course search biologists would be the completion of that the statistician through his knowledge of a 1-year conrse at the level of Snedecor (1956) statistical procedures can sometimes help sal- or Steel and Torrie (1960). Many of the un- vage some results from an otherwise hope- derlying principles and most of the mathe- lessly muddled experiment. matical derivations obviously would not be One point must be made clear at the outset learned. Yet the experimenter would become --the statistician is not a tool of the experi- more aware of the limitations as well as the menter although statistics is such a tool. potentials of statistics as a tool. Most biolo- What then is the proper role of the statisti- gists who have not had such formal training cian in biological experimentation? In this can acquire an equivalent understanding time of specialization, most professional men through independent study. and women are highly trained in one aspect of It is desirable for the statistician on the one field of interest. The old clich6 that con- other hand, as a member of the team, to have tinued education is simply "Learning more some acquaintance with the field of the biolo- and more about less and less" is nevertheless gist. Many biometricians who have come up cogently descriptive of our age. through biologically oriented curricula, with Many biological problems, however, overlap later specialization in statistics, will have this various areas of specialization. A team con. acquaintanceship. Statisticians with formal sisting of many specialists may be needed to mathematical training, on the contrary, do not solve such problems. A not unusual team in often have a biological background. A general the area of hormone secretion and function observation, however, is that it is much easier may include an endocrinologist, a biochemist, for a mathematician to gain a general knowl- a physiologist, a physicist, and should we not edge of biology than for a biologist to gain a include, also, either a biomathematician or a general knowledge of mathematics. statistician? An example of such a team is the What then can the statistician offer as a group which postulated the structure of ge- member of the team? netic material. All statisticians will not agree, since Fisher In this sense the statistician should be con- has remarked that variance is a term describ- sidered as a partner of the team, if not a full ing the attitude of one statistician to another. partner, at least a junior partner. He should Practicing biometricians, however, find the not be in the position of a lawyer who. wants in following statements by Cochran and Cox at the beginning of a case, not just after the (1957) to be descriptive of the problems they client has been convicted. face everyday: This does not imply that the biochemist or "Statisticians are often asked for advice in physiologist needs no training in mathematics making inferences from the results of experi- or statistics. An appreciation of the problems ments. Since the inferences that can be made and techniques of statistics is important for depend on the way in which the experiment all experimenters. This knowledge may be no was carried out, the statistician should request 1 Invitational paper presented at the 56th Annual Meeting a detailed description of the experiment and of the American Society o.f Animal Science, Knoxville, Tennessee. its objectives. It may then become evident 559 560 VAN VLECK AND HENDERSON that no inferences can be made or that those ferences among treatments if such differences which can be made do not answer the ques- actually exist. It should be common knowl- tions to which the experimenter had hoped to edge that statistically significant differences find answers. In these unhappy circumstances, can nearly always be found in biological ex- about all that can be done is to indicate, if perimentation, if the numbers of replicates are possible, how to avoid this outcome in future large enough 9r if the significance level for re- experiments. Consequently, it has come to be jection of the hypothesis of no differences is set realized that the time to think about statisti- at a sufficiently high level. This aspect of sig- cal inference, or to seek advice, is when the nificance levels will be discussed more fully a experiment is being planned. little later. "Participation in the initial stages of experi- Kempthorne (1952) and Federer (1955) ments in different areas of research leads to a both suggest in their books a similar set of strong conviction that too little time and effort general principles of experimentation. Kemp- is put into the planning of experiments. The thorne (1952) states that "a statistically de- statistician who expects that his contribution signed investigation may be said to consist of to the planning will involve some technical the following steps: matter in statistical theory finds repeatedly 1. Statement of the problem. that he makes a much more valuable contribu- 2. Formulation of hypotheses. tion simply by getting the investigator to ex- 3. Devising of experimental techniques and plain clearly why he is doing the experiment, design. to justify the experimental treatments whose 4. Examination of possible outcomes and effects he proposes to compare, and to defend reference back to the reasons for the in- his claim that the completed experiment will quiry to be sure the experiment provides enable its objectives to be realized. For this the required information to an adequate reason the remainder of this chapter is devoted extent. to some elementary comments on the subject 5. Consideration of the possible results from of planning. These comments are offered with the point of view of the statistical pro- diffidence, because they concern questions on cedures which will be applied to them, to which the statistician has, or should have, no ensure that the conditions necessary for special authority, and because some of the these procedures to be valid are satisfied. advice is so trite that it would be unnecessary 6. Performance of experiment. if it were not so often overlooked. 7. Application of statistical techniques to "It is good practice to make a written draft experimental results. of the proposals for any experiment. This draft 8. Drawing conclusions with measures of will in general have three parts: (1) a state- the reliability of estimates of any quanti- ment of the objectives; (2) a description of ties that are evaluated, careful considera- the experiment, covering such matters as the tion being given to the validity of the experimental treatments, the size of the ex- conclusions for the population of objects periment, and the experimental material; and or events to which they are to apply. (3) an outline of the method of analysis of 9. Evaluation of the whole investigation, the results." particularly with other investigations on Along with the statements of objectives, hy- the same or similar problems." potheses to be tested, and effects to be esti- mated there should be a literature review of Experienced and outstanding investigators similar or related experiments.