GRE 作文题目
来源于朗播用户：ZHAO
It is a grave mistake to theorize before one has data.
题目分析
翻译
在获得数据前就建立理论是个严重的错误。
指导
本题讨论数据对于建立理论的必要性问题，题目认为没有数据就建立理论是不对的。在分析的过程中，可以从数据对于理论的作用、数据和理论之间的充分关系和必要关系、建立无数据理论错误的原因、对于不同时期不同学科的问题等方面展开思考。需要注意题目讨论的是数据对于理论建立的必要性（不满足A，必然不B；满足A，不必然B），而不是充分性（满足A，必然B；不满足A，不必然B）。同时，需要注意假说、理论、定律三个概念之间的区别——HYPOTHESIS implies insufficient evidence to provide more than a tentative explanation. THEORY implies a greater range of evidence and greater likelihood of truth. LAW implies a statement of order and relation in nature that has been found to be invariable under the same conditions.
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1. 请举例并分析：是否存在建立某理论的过程中没有数据支撑的事例？该理论在以后的实践过程中的情况是如何的？
回答：
the Theory of Relativity.改变了物理学的发展方向和人们对宇宙的认识。
2. 数据在理论建立的过程中起到哪些作用？请举例并简述。
回答：
数据可以对理论的建立起到指导和检验的作用。例子？
3. 数据在理论建立过程中起到的作用是否不可替代或不可缺少？如果是，为什么？如果不是，哪些可以缺少或被其他内容替代？
回答：
不是。数据用来检验理论。理论可以是对现实的猜想，然后用数据检验。
4. 没有数据支撑的理论可能会在哪些方面存在隐患或出现问题？请结合具体事例阐述。
回答：
没有数据支撑的理论很可能是错误的。
其他用户的回答
作文
Will theorizing before one has data certainly be a huge mistake? In my opinion, data is helpful but not always necessary to establish a theory, and the very value of data is to test a theory.
There are many cases in the history of science that scientists, by generalizing collected data, established great theories. For instance, Mendal spent 8 years on observing the genetic characters of pea. With the large amount of data he had collected, he generalize the Laws of Heredity, which made him the Father of Genetics. It is obvious that without the data, it's impossible for Mendal to establish his theory which laid the foundation of modern genetics. So it seems that data is quite necessary to theorize and it is a mistake to theorize without data.
However, it is a hasty generalization to make such a conclusion. In fact, data is not always necessary for one to theorize. Some scientists can also establish great theories without data. When establishing the Theory of Relativity which had a profound effect on modern physics, Einstein had no data available to ask for help. Another example is Stephen Hawkin, who is considered to be today's greatest scientist. His theory about the origin of the universe, which was established without the support of data, has brought an impact to people and shown the essence of the universe. So it is also possible for other scientists to build theories of great significance.
That without data scientists can still build profound theories don't indicate that data is of little importance. As mentioned above, data is helpful for scientists to make gneralization and establish theories. In fact, the very value of data is to test a theory. Scientists may advance a theory without the help of data, however, the validity of such a theory is open to doubt. Without the support of data, it is impossible for others to accept it. For instance, when the Theory of Relativity was advanced, it was not accept by the academic field and the public until solid proof was found. Hawkin's theory is in a similar situation. Although it explains many phenomena, there are still some scientists refuse to accept it because the lack of persuasive data. Moreover, some may argue that theorizing before one has data can lead to a false one. However, when such a theory is advanced, its validity can be tested by data collected afterwards.
Although data is helpful to theorize, it does not necessarily mean that without data, one can never establish a theory of great significance. And it can only be a mistake when a theory is not tested through data.
There are many cases in the history of science that scientists, by generalizing collected data, established great theories. For instance, Mendal spent 8 years on observing the genetic characters of pea. With the large amount of data he had collected, he generalize the Laws of Heredity, which made him the Father of Genetics. It is obvious that without the data, it's impossible for Mendal to establish his theory which laid the foundation of modern genetics. So it seems that data is quite necessary to theorize and it is a mistake to theorize without data.
However, it is a hasty generalization to make such a conclusion. In fact, data is not always necessary for one to theorize. Some scientists can also establish great theories without data. When establishing the Theory of Relativity which had a profound effect on modern physics, Einstein had no data available to ask for help. Another example is Stephen Hawkin, who is considered to be today's greatest scientist. His theory about the origin of the universe, which was established without the support of data, has brought an impact to people and shown the essence of the universe. So it is also possible for other scientists to build theories of great significance.
That without data scientists can still build profound theories don't indicate that data is of little importance. As mentioned above, data is helpful for scientists to make gneralization and establish theories. In fact, the very value of data is to test a theory. Scientists may advance a theory without the help of data, however, the validity of such a theory is open to doubt. Without the support of data, it is impossible for others to accept it. For instance, when the Theory of Relativity was advanced, it was not accept by the academic field and the public until solid proof was found. Hawkin's theory is in a similar situation. Although it explains many phenomena, there are still some scientists refuse to accept it because the lack of persuasive data. Moreover, some may argue that theorizing before one has data can lead to a false one. However, when such a theory is advanced, its validity can be tested by data collected afterwards.
Although data is helpful to theorize, it does not necessarily mean that without data, one can never establish a theory of great significance. And it can only be a mistake when a theory is not tested through data.
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