It is clear that Economics, if it is to be a science at all, must be a mathematical science ... simply because it deals with quantities... As the complete theory of almost every other science involves the use of calculus, so we cannot have a true theory of Economics without its aid.
Interpretation
Economics should utilize mathematical principles to become a true science.
In this quote, William Stanley Jevons asserts that for economics to be considered a legitimate science, it must incorporate mathematical methods, particularly calculus. He emphasizes that just as other scientific fields rely on mathematical theories to analyze and understand complexities, economics too requires these tools to properly address its fundamental quantities and relationships.
In practice
In a lecture on the importance of math in social sciences, you might quote Jevons to emphasize your point.
Whoever wishes to acquire a deep acquaintance with Nature must observe that there are analogies which connect whole branches of science in a parallel manner, and enable us to infer of one class of phenomena what we know of another. It has thus happened on several occasions that the discovery of an unsuspected analogy between two branches of knowledge has been the starting point for a rapid course of discovery.
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As well might it be said that, because we are ignorant of the laws by which metals are produced and trees developed, we cannot know anything of the origin of steamships and railways
That is the way of the scientist. He will spend thirty years in building up a mountain range of facts with the intent to prove a certain theory; then he is so happy with his achievement that as a rule he overlooks the main chief fact of all-that all his accumulation proves an entirely different thing.
Quantum mechanics brought an unexpected fuzziness into physics because of quantum uncertainty, the Heisenberg uncertainty principle. String theory does so again because a point particle is replaced by a string, which is more spread out.
The problem with data is that it says a lot, but it also says nothing. 'Big data' is terrific, but it's usually thin. To understand why something is happening, we have to engage in both forensics and guess work.
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