LSAT 127 – Section 1 – Question 10

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December 2003

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Question QuickView	Type	Tags	Answer Choices	Curve This gives you an idea of how this question is “curved”— in other words, what level of scorer is likely to get this question right. It shows a model of scaled LSAT score vs probability of getting the question correct. It is based on student test results for each question. The gray bar extends from the score for a 25% chance, to the score for a 75% chance of getting the question correct. The black line in the middle is the score for a 50% chance of getting the question correct. The percentage at the right is the expected chance of someone with the same score as you on that PrepTest getting that question correct	Question Difficulty	Psg/Game/S Difficulty	Explanation

Question QuickView	Type	Tags	Answer Choices	Curve This gives you an idea of how this question is “curved”— in other words, what level of scorer is likely to get this question right. It shows a model of scaled LSAT score vs probability of getting the question correct. It is based on student test results for each question. The gray bar extends from the score for a 25% chance, to the score for a 75% chance of getting the question correct. The black line in the middle is the score for a 50% chance of getting the question correct. The percentage at the right is the expected chance of someone with the same score as you on that PrepTest getting that question correct	Question Difficulty	Psg/Game/S Difficulty	Explanation
PT127 S1 Q10	+LR +Exp	Main conclusion or main point +MC Value Judgment +ValJudg	A 9% 159 B 3% 155 C 1% 152 D 86% 163 E 0% 147	129 141 153	+Easier	147.168 +SubsectionMedium

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J.Y.’s explanation

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Prediction, the hallmark of the natural sciences, appears to have been made possible by reducing phenomena to mathematical expressions. Some social scientists also want the power to predict accurately and assume they ought to perform the same reduction. But this would be a mistake; it would neglect data that are not easily mathematized and thereby would only distort the social phenomena.

Summarize Argument: Counter-Position

The author claims that reducing phenomena to mathematical expressions in the social sciences would be a mistake, despite what some social scientists assume. The predictions would leave out social science data that is not easy to reduce to those expressions, so the predictions of social phenomena would be inaccurate.

Identify Conclusion

The conclusion is the author’s opinion of using reducing phenomena to mathematical expressions in the social sciences: “this would be a mistake;”

The social sciences do not have as much predictive power as the natural sciences.

The author does not discuss predictive power, only what is compatible with being reduced to mathematical formulas for prediction.

Mathematics plays a more important role in the natural sciences than it does in the social sciences.

The author does not claim where math plays a more important role. He only claims that reducing phenomena to mathematical expressions for prediction purposes in the social sciences is a mistake.

There is a need in the social sciences to improve the ability to predict.

This is not contained in the stimulus. As part of the context, we know some social scientists want the power to predict accurately, but that is all. We don’t know anything about a need for improvement,

Phenomena in the social sciences should not be reduced to mathematical formulas.

This accurately paraphrases the conclusion. The author says “this” (reducing phenomena in the social to mathematical expressions) “would be a mistake” (should not happen).

Prediction is responsible for the success of the natural sciences.

In the context, we are told prediction is the “hallmark” of the natural sciences. This is an inaccurate paraphrase of that context.

LSAT 127 – Section 1 – Question 10

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J.Y.’s explanation

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LSAT PrepTest 127 Explanations

Section 1 - Logical Reasoning

Section 2 - Logical Reasoning

Section 3 - Logical Reasoning

Section 4 - Reading Comprehension

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