[i习作temp] issue184，含泪求拍 [复制链接]

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发表于 2009-8-15 21:11:04 |只看该作者 |倒序浏览

人生中第二篇issue，字数不多，分领域讨论，求高手狠拍！
不胜感谢！

题目：ISSUE184 - "It is a grave mistake to theorize before one has data."
字数：477 用时：xx:xx:xx 日期：2009/8/15 1:21:44

Will it be a serious mistake if researchers carry out a theory withoutgetting sufficient data? As a common sense, researchers in differentfields will be faced with sometimes utterly different situations wherethere does not exist a unique method in researches between data andtheories. Moreover, to evaluate a theory only by criteria of data isirresponsible and even dangerous.

In some disciplines such as economics, statistics and demographyresearchers have to deal with a myriad of data before proposing aprecise theory, otherwise any conclusions are inevitably unconvincing.As for an economic professor's recent study, he has been making effortson collecting and working on data for six years before publishing thefinal theories. It always takes years for a nation to put up census andspends months dealing with the data statistically before maintain awhole demographic characteristic of the populace in the nation. So itis always in subjects involving a great number of objects to researchon that data becomes the vital factor before deducing the theory.

While in other fields, such as theoretical mathematics and physics, itis another case at all. In the foregoing two kinds of disciplines, itis sometimes good to conjecture than to research into data carefullyfor inspiration plays a more significant role in such fields than indisciplines which a lot of statistics is involved. Quantum mechanics,for instance, is based on several brief postulates which are almostresults of pure reasoning without data by a group of scientists such asPlanck, Schrodinger, and Heisenberg. Oppositely, results of experimentsafterward gradually substantiate the validity of quantum mechanicsitself. A more common example is abstract algebra, a field initiated byAbel, Galois and other mathematicians by a method of reasoning step bystep and finally builds up the edifice of abstract algebra. In thesecases, reasoning instead of data plays the key role and redundant datamay otherwise be obstacles in reasoning.

Besides the two different sorts of disciplines in which data might beeither significant or unimportant, there are still fields whereseemingly no data is available such as humanities, philosophy and art.In these fields, people work by logical reasoning or artistic craftswhich have seldom anything to do with data and statistic and it will beno doubt unfair to estimate a theory in such fields in perspective ofdata.

In the finally analysis, one cannot simply contends that theories arenot correct ones which comes out based on no much data. Moreover, it isnot rational to hastily deny a new theory for mere reason that not muchdata is mentioned in the process of reasoning. In my point of view, adesirable method to evaluate a theory should be something paying moreattention to the stringency of reasoning and deducing than on whethermuch data is worked on in research processes.