Ok, some really ARE geniuses. But a lot of researchers use a number of standard tools across many scientists I think I’ve never mentioned in order to do most of their statistical, mathematical, and/or quantitative work. So I figured I’d provide a quick post with some of the names of these tools so that any curious can use Wikipedia or the official webpages to get a glimpse behind the curtain and through the looking glass.
For those in sciences from the medical sciences and neurosciences to sociology and economics, most of the tools are statistical software packages. The most popular are SPSS, R, & SAS, but Excel, Stata, Statistica, minitab, and many others are available for free or for a LOT of money.
Then there are computer algebra systems like Mathematica and Maple. These are actually used more for teaching mathematics, statistics, and research methods then for such purposes. However, they can be useful for researchers too in certain circumstances.
Then there is the powerhouse: MATLAB. The name stands for Matrix Laboratory and it is the tool for all those who require not just statistics but computational methods, whether engineers, systems biologists, biophysicists, etc. There are other packages, such as SciPy, NumPy, or better yet SageMath (which combines both of the last two and many other free packages for free; the problem is it isn’t easily used for those who have Windows as an OP).
The point is simply that while all of us have probably used Excel and a graphing calculator, the tools researchers use when it comes to statistical software packages, computer algebra systems, computational software packages, and in general any scientific computing environments are far, far more powerful. Without them, most of the best (and worst) statistical/computational methods in scientific research would be impossible (humans can’t perform the relevant computations fast enough).
Bottom line: don’t be to impressed just because you see some really impressive mathematics in some research paper or a summary of it. Maybe it is impressive, or maybe a few researchers who had no clue what they were doing plugged numbers into software and it pooped out results.