The kind of mathematics used by scientists in analyzing data is statistics. One purpose of statistics is to support that the data collected is meaningful from which to draw conclusions. Take for instance an experiment measuring which direction animal will turn if startled. The first set of data has two points; one turning left and one turning right. The researcher concludes the animal will turn right 50% of the time. Can the researcher make such conclusions based on two data points? Not really. Statistics can show that only having the two points does not constitute meaningful results. The researcher now has a second set of data with the animal turning 2,112 times to the left and 6,463 times to the right out of a total of 8,575 times. The researcher can conclude with data support that the probability the animal will turn right when startled is 75.3% of the time. Other statistical terms and tools are median, mean, mode, standard deviation, etc.. All play their role in helping researchers produce meaningful data analyses.

Keep in mind that other types of math, like algebra, calculus, differential equations, and more, are used to explaining results and modelling phenomena, but good general data analysis always involves statistics.