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Science, Theory, Simulation Models, Statisitcs

Science, theory, simulation models, statistics, experiments



The following definition is adapted from “Webster’s New World Dictionary” (1988).

Science [Ofr < L scientia, prp. Of scire, to know, orig., to discern, distinguish ]

1.   orig., the state or fact of knowledge; knowledge.

2.   systematized knowledge derived from observation, study, and experimentation carried on in order to determine the nature of principles of what is being studies.

3.   a branch of knowledge or study, esp. one concerned with establishing and systematizing facts, principles, and methods, as by experiments and hypotheses, e.g., the science of mathematics.

4.   the systematized knowledge of nature and the physical world and any branch of this.

5.   skill of technique based upon systematized training.

Critical evaluation -- the only reliable road to knowledge. 

Harper, A. E. 1990. Critical evaluation - the only reliable road to knowledge. BioScience 40(1): 46-47.

Oliver, D. J. 1990. Letters - Critical evaluation? BioScience 40(6): 421.

Popper, K. 1989. Logik der Forschung. J.C.B. Mohr (Paul Siebeck) Tübingen.




The following definitions are adapted from “Webster’s New World Dictionary” (1988).

Theory [< Fr or LL: Fr théorie < LL theoria < Gr theōria, a looking at, contemplation, speculation, theory < theōrein: see theorem]

1.   orig., a mental viewing, contemplation,

2.   a speculative idea or plan as to how something might be done,

3.   a systematic statement of principles involved [the theory of equations in mathematics],

4.   a formulation of apparent relationships or underlying principles of certain observed phenomena which has been verified to some degree,

5.   that branch of an art or science consisting in a knowledge of its principles and methods rather than in its practice; pure, as opposed to applied, science, etc.

6.   popularly, a mere conjecture, or guess.

Among these definitions, I use the 3rd, 4th and 5th in theoretical ecology.

Theory, as compared here, implies considerable evidence in support of a formulated general principle explaining the operation of certain phenomena, e.g., the theory of evolution.

Hypothesis implies an inadequacy of evidence in support of an explanation that is tentatively inferred, often as a basis for further experimentation, e.g., the nebular hypothesis.

Law implies an exact formulation of the principle operating in a sequence of events in nature, observed to occur with unvarying uniformity under the same conditions, e.g., the law of the conservation of energy.



Simulation models

Model is only model.  Models are useful to handle tedious calculation, to simulate complex system, to enable people to “see” what they cannot see through human eyes.  However, a model will be not better than a computer game, if it is not based on solid theory.  A model cannot make precise prediction, if it is not based on robust data.  Finally, a model must be user-friendly, i.e., it must be designed in Visual BASIC or C.  An user-friendly model is also helpful to let people to apply a theory to their own data, even though the user does not understand the theory.  However, it is dangerous to use a theory without thorough understanding.




Although I do think statistics is important to most routine scientific works.  I really like the following two says about statistics. (I learned them from AWAD).

Statisticians know that if you put a man’s head in a sauna and his feet in a deep freeze, he will feel pretty good - on the average.

Statistics are used as a drunk uses lampposts--for support, not illumination.



Experiment is one of the important ways in scientific reasoning.  But, it is not the only one.  Experiments take money, labor and time.  Among them, time is a limiting factor.  Scientists do not live longer than others.  If 50 insects are enough for a study, we should not use 60.  If three replicates are enough, why should we do four replicates?  We need more time to do the most important work, interpretation and thinking, or reasoning.  If we can do “abstract reasoning”, we can cut the experiments to minimum.


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