Big Data is a big deal. Businesses want to collect analytics so that they can provide targeted advertising. The government has invested millions of dollars in large-scale projects cataloguing the microbiome, the epigenome, the brain, and genetic data banking. Medicine is turning to large-scale data collection as a way to improve clinical trials, preemptively address individual’s health problems, and find ways to cure diseases. Chris Anderson of Wired asked in a 2008 article whether Big Data marked the end of theory and the scientific method. Since then there has been an influx of papers addressing whether Big Data changes the way we do science, if it is a new paradigm, or if it is only one part of the scientific method. This paper will begin by taking a broad view of Big Data, defining what it is and what the underlying assumptions are. Then we will look at how Big Data affects the scientific method, addressing various sides of the debate as to whether Big Data negates the need for hypothesis-driven science. Finally, we will take a philosophical look at how Big Data, as applied to the scientific method, is really a new way to attempt to accomplish old goals of trying to obtain objective Truth and predicting the future. Throughout this paper, I will use current initiatives in clinical medicine, genetics, and biosciences to illustrate Big Data in scientific research.