1. Limited time only! Sign up for a free 30min personal tutor trial with Chegg Tutors
    Dismiss Notice
Dismiss Notice
Join Physics Forums Today!
The friendliest, high quality science and math community on the planet! Everyone who loves science is here!

The difference between analysis and statistics as they relate to physics

  1. May 19, 2012 #1
    I'm going to try for a triple major in physics, math, and computer science. I already have a CS related associates, so many of those courses as well as electives are knocked out already. The plan is to master in astrophysics/cosmology, and the end goal is to work for NASA.

    I read in another thread that many people suggested statistics courses would be helpful, but I also read a few votes for analysis. In the math portion of the major, I'll have to choose between a statistics sequence and an analysis sequence.

    Because of all the comments, I'm already leaning toward going the statistics sequence route, but am curious as to what the difference is in how they relate to the field. In what way would you use statistics in your job and how often. I'm wondering the same for analysis.


    I'm also thinking that for my first academic year it would be a good idea to do work study on campus with the computer science department and then go for a summer internship in the same field, but the following years, to do my work study/internships in the physics field. The only problem is that the only things I can really think of where I live that would be close is defense aerospace. Is this a good strategy? Any ideas on this front?
    Last edited: May 19, 2012
  2. jcsd
  3. May 19, 2012 #2


    User Avatar
    Science Advisor

    Hey placateddoll and welcome to the forums.

    I can't comment on the analysis route since I don't have enough information, but I do have some comments with regard to statistics in a general sense.

    We are moving generally from the old Newtonian analytic framework to the Quantum analytic framework: in other words, we are moving from certainty to uncertainty and in order to effectively qualify and quantify this kind of thinking, results, and our conclusions we have to use probability and statistics.

    As a result statistics is becoming the new way to analyze things in scientific, engineering, and other forms of analytic endeavors. Effectively statistics is used to analyze any form of data under uncertainty and reduce it to characteristics which are manageable, yet describe enough about the data, model, or an inference based on the data.

    In terms of using it in a job, if you have to make sense of data under uncertainty of any kind, then you will use it in any kind of analysis of that sort. The specifics of the use will depend on what kinds of things you are analyzing, what assumptions you make, what models/techniques/algorithms etc you use and the industry/domain you are working in.

    Also on top of the above, the kind of work you do and what its used for will make a difference in how you go about your work. Generally the more severe the consequence for getting something wrong, the more care taken in not only obtaining but also checking the results. Of course this doesn't always happen this way, but I think its a fair guide for this kind of thing.

    Depending on what you do, you may need to know both statistics and analysis (as in real analysis, functional analysis and so on) for applied work (like in finance), but I would imagine that for physics and related applications, it's more important to know the applied aspects of the math over the pure ones simply because the focus is on application and not so much on theory.
  4. May 19, 2012 #3
    Thanks for that explanation; it makes a lot of sense. I was already leaning in the direction, but that pretty well cements it for me. Besides, I'm not unaware that this will be a heavy course load, and the statistics sequence is a total of 9 credit hours, whereas the analysis sequence would be a total of 15.
Share this great discussion with others via Reddit, Google+, Twitter, or Facebook