GRE 作文题目
来源于朗播用户：孙熙领
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：
不存在
任何理论的建立都需要数据的支撑，没有数据的支撑就不能称之为理论。
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：
数据在理论的建立过程中起到了证明理论正确性的作用。如果没有数据支持理论，那理论基本是谬论。
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：
不可代替
因为要证明理论的正确性，就必须有数据说明其正确性。
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：
可能理论成立只是个例，没有代表性。
其他用户的回答
作文
Is it a grave mistake to theorize before one has data? I do not agree with this statement. It is no wonder that data is so important to theory that they can prove the truth of theory. However, data are not necessary for theory because some theories can derive from theory analysis and deduction. And these theories are also scientific.
First of all, data have been known to be so important for theory that they can strengthen the validity of theory and persuade people to believe the correctness of theory. Generally, people do not have specific knowledge to judge whether theory is right. It is just data that give people evidence to believe theory. Mendel present a relevant case for analysis. Mendel is a genetic scientist in 19th century. He had done his research through kinds of experiments to find the Genetic Law. After eight years experiments, he finally gathered enough data to deduct the Genetic Law which is the basis of other genetic theories. In Mendel's process of deducting the Genetic Law, data played an important role. Therefore, data are significant for theory.
However, data are not necessary for concluding theory. Despite science and technology have a huge development in the present, there are still a huge surplus of experiment condition that people can not achieve. For example, we can not measure the range of universe and we can not achieve a speed equaled to that of light. So many scientist will fulfill their theory through theory analysis and deduction, which is also rigorous and correct. A good example of this view point is theory of relativity. Theory of relativity which is the most important theory in 20th century can not be proved by data because of lacking of experiment conditions. Its author Einstein could not achieve a object whose speed equal to speed of light. However, although lacking of relatively data to support his theory, Einstein made a rigorous deduction in the basis of other theories that have been proved their validity. Finally, theory of relativity become the most important theory and majority people believe it.
Another example of theory that lacks data to support involves many basic physics experiment. In high school, almost every children learn a subject called physics. When teachers teach the chapter about movement, they will tell students that some theories base on the assumption that the ground is smooth and there is no friction between object and ground. Under this assumption, students will learn a huge surplus of theories about movement, such as Newton's First law of Motion. However, there is no an ideal technique for creating a environment without friction now. Therefore, such theories based on the assumption of no friction lack data to support themselves. But they are correct because of their rigorous deduction. These are only two examples of why data are not necessary for theory.
Therefore, although data are significant for discovering and supporting theory, they are not necessary for theory.
[20:25 - 21:17] -> 52mins, 491.
First of all, data have been known to be so important for theory that they can strengthen the validity of theory and persuade people to believe the correctness of theory. Generally, people do not have specific knowledge to judge whether theory is right. It is just data that give people evidence to believe theory. Mendel present a relevant case for analysis. Mendel is a genetic scientist in 19th century. He had done his research through kinds of experiments to find the Genetic Law. After eight years experiments, he finally gathered enough data to deduct the Genetic Law which is the basis of other genetic theories. In Mendel's process of deducting the Genetic Law, data played an important role. Therefore, data are significant for theory.
However, data are not necessary for concluding theory. Despite science and technology have a huge development in the present, there are still a huge surplus of experiment condition that people can not achieve. For example, we can not measure the range of universe and we can not achieve a speed equaled to that of light. So many scientist will fulfill their theory through theory analysis and deduction, which is also rigorous and correct. A good example of this view point is theory of relativity. Theory of relativity which is the most important theory in 20th century can not be proved by data because of lacking of experiment conditions. Its author Einstein could not achieve a object whose speed equal to speed of light. However, although lacking of relatively data to support his theory, Einstein made a rigorous deduction in the basis of other theories that have been proved their validity. Finally, theory of relativity become the most important theory and majority people believe it.
Another example of theory that lacks data to support involves many basic physics experiment. In high school, almost every children learn a subject called physics. When teachers teach the chapter about movement, they will tell students that some theories base on the assumption that the ground is smooth and there is no friction between object and ground. Under this assumption, students will learn a huge surplus of theories about movement, such as Newton's First law of Motion. However, there is no an ideal technique for creating a environment without friction now. Therefore, such theories based on the assumption of no friction lack data to support themselves. But they are correct because of their rigorous deduction. These are only two examples of why data are not necessary for theory.
Therefore, although data are significant for discovering and supporting theory, they are not necessary for theory.
[20:25 - 21:17] -> 52mins, 491.
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