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Meteorologists say that if only they could design an accurate mathematical model of the atmosphere with all its complexities, they could forecast the weather with real precision. But this is an idle boast, immune to any evaluation, for any inadequate weather forecast would obviously be blamed on imperfections in the model.

Which of the following, if true, could best be used as a basis for arguing against the authorâ€™s position that the meteorologistsâ€™ claim cannot be evaluated?

(A) Certain unusual configurations of data can serve as the basis for precise weather forecasts even though the exact causal mechanisms are not understood.
(B) Most significant gains in the accuracy of the relevant mathematical models are accompanied by clear gains in the precision of weather forecasts.
(C) Mathematical models of the meteorological aftermath of such catastrophic events as volcanic eruptions are beginning to be constructed.
(D) Modern weather forecasts for as much as a full day ahead are broadly correct about 80 percent of the time.
(E) Meteorologists readily concede that the accurate mathematical model they are talking about is not now in their power to construc
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Meteorologists's claim = precise weather forecast is possible

Author's claim = This mathematical model will make inadequate weather forecast (and will be blamed on imperfections)

Basis for counterattack = Precise weather forecast is possible thanks to certain unusual configurations of data (although nobody knows how it is possible, it is possible)

i take B by POE. it cant be A because A implies that : at times , with certain sets of data , it is possible to predict weather accurately, even when "the exact causal mechanisms are not understood. " --> which means that the results of a mathematical model dont justify the results produced by these sets of data. This choice does in no way state that the accurate mathematical models can be created...which is what the author is challenging.

Certain unusual configurations of data can serve as the basis for precise weather forecasts even though the exact causal mechanisms are not understood.
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shud be B 'coz B states that there are earlier gains in the prediction due to gains in accuracy. Hence, its tested.
The author brushed off the meteorologists' claim saying their claim is immune to evaluation. Hence, we can say the above statement wakens the author's claim.

I picked B because B shows that the predictive ability of the mathmatical model can be scrutinized. The exact opposite of what the author of the argument claims.
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say that if only they could design an accurate mathematical model of the atmosphere with all its complexities, they could forecast the weather with real precision.

A) Certain unusual configurations of data can serve as the basis for precise weather forecasts even though the exact causal mechanisms are not understood.
(B) Most significant gains in the accuracy of the relevant mathematical models are accompanied by clear gains in the precision of weather forecasts.

I go with B. Why?
The passage talks about accurate mathematical model and weather forecast woth real precision. A talks about the "precise weather forecast" and "casual mechanisms that are not understood", which is the opposite to accurate mathematical model. B links the accuracy of the relevant mathematical models to the gains in precision of weather forecasts. And this link is done in a proper way.

I admit that (B) links the accuracy of the math model and the gains in the precision of weather forecast.

But still, (B) says that the math model is correct under a certain condition - a condition that is only supported by the earlier gains of weather forecast.

Therefore, according to (B), the math model is NOT PERFECT, and will be possibly blamed.

I admit that (B) links the accuracy of the math model and the gains in the precision of weather forecast.

But still, (B) says that the math model is correct under a certain condition - a condition that is only supported by the earlier gains of weather forecast.

Therefore, according to (B), the math model is NOT PERFECT, and will be possibly blamed.

What do you think?

Though the current model is not perfect, gains in accuracies have been directly related to improvements in the model. So, it follows that once the model is perfected, inaccuracies in forecast cannot be blamed on the model itself.
Hope that clears it up!