Why science must use math The methodology of science has materially benefited humanity by increasing the knowledge of the universe by means of incremental progress. Year to year, in every area of study, what we actually know is non-decreasing. Consequent to the success of this methodology, professional scientists have been viewed as authorities in their individual areas of study and great weight is put on their opinions -- sometimes even when that opinion is not clearly based on their field of expertise. Lots of people would like people to seek out their opinions, but comparatively few have the resources, inclination and desire to rise to prominence as a professional scientist in any particular field of study. Those that seek to partake of the authority of professional scientists without engaging in the actual methodology of scientists we call pseudo-scientists. One class of pseudo-scientist rests upon worthless "credentials" from a non-mainstream educational body, often for a price -- those are simple frauds. Another class of pseudo-scientist apes the form of scientific practice and may devote time to writing "papers" or seeking "publication" in a journal of ill-repute -- those we call cargo-cult pseudo-scientists after stories of South Pacfic islanders building shrines in the form of airport control towers speaking to non-existent cargo-delivering aircraft on non-functional "radios" made of woven plant matter. Yet another class of pseudo-scientist spends time trying to explain the behavior of phenomena that have not been documented to exist -- after Harriet Hall, we call those tooth-fairy psuedo-scientists after the example of someone who tried to work out the details of how a class of mythological beings obtained steady sources of local currency and for what purpose they sought to exchange currency for human deciduous teeth. A related behavior is science denialism -- the outright denial that science is progressively adding to human understanding, that it is of benefit or that it is reliable. For both pseudo-scientists and denialists, a common trait which distinguishes this anti-science camp from professional scientists is antipathy and distrust of math. Here, I seek to explain that math is a necessary part of science and a large part of the reasons professional scientists are widely regarded as authorities. I. What are the purposes and goals of science Since the begining of recorded thought, mankind has recorded stories about what they thought Nature was created for -- birds sing because they were made for the beauty of their song or wood was made to float or goats were made to be tasty when goat parts, garlic and chili powder are placed on hot metal. Back in the 17th century, Bacon rejected that egocentric tradition of teleological story telling in favor of proceeding empirically to accumulate and organize data and work out inferences from that data. Further such inferences can be tested by further observation to see that are reliable. a. To gain knowledge of phenomena which occur in the Universe Gathering data is a goal unto itself. b. To organize and unify knowledge of phenomena which occur in the Universe A hypothesis which explains many events reliably is easier to teach and learn than a great many events unconnected by unifying principles. c. To gain influence for humanity over the operation of the Universe "Knowledge is power" - Bacon If we have communicable, reliable and precise descriptions of the behavior of nature, then we can predict in detail what happens when we make choices. And if we can predict what the outcome of various choices are, then we can decide objectively between the choices. Then, of these choices, we can choose the best alternative at the cheapest cost. What more power could one aspire to? II. What is the methodology of science Observation, Model-making, Testing and Unification are important parts of scientific methodology. a. Observe a phenomena Scientists don't just observe. They observe repeatedly and in detail. They gather data that is obviously relevant and data that almost certainly is not relevant. They gather data when the object of their study is present and also when it is absent, all the better to compare with. b. Make a hypothetical model predicting aspects of the phenomena From the great mountain of data, a trend, a cluster of points, a distribution or a c. Test and reject models that don't reliable predict the phenomena d. Where possible, unify models to explain multiple phenomena III. Is a fragile model of more or less use to science than a flexible and ambiguous model? IV. Why are mathematical models fragile? V. Is this fragility generally seen in other aspects of mathmatics? VI. Is this fragility generally seen in Literature or Philosophy? VII. What are the results of math-based science?