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Is it possible to defend a Ph.D. thesis in statistics without proving any theorems? I think the answer is affirmative since I've seen a couple of theses in that fashion. However, I'm unaware of how common/rare they are.

My follow-up question is: If I doubt (and I have good reasons to do so) my abilities to prove any original results going on towards my last Ph.D. year, should I:

i) notify my advisor about it, or ii) just go through the motions (perhaps attempting to prove results as my advisor instructs me) and focus my effort on simulation studies and real-world applications.

Under many circumstances, if the advisor in question is somewhat empathetic, i) would be the better answer, as they would possibly provide alternative theoretical problems to be solved or focus their effort on simulation studies and applications. However, I'm afraid my advisor might simply give up supervising me (which again, I have good reasons to believe). That makes option ii) more tempting. The risk with this approach is that they could simply not allow me to defend a thesis without any theoretical results.

Now I will pre-emptively answer some questions that might be asked:

Why do you doubt your abilities to prove original results?: First of all, my background is not in statistics, so I feel that my "intuition" with regard to theorems in probability and statistics is behind my peers. Moreover, I haven't been able to prove even the simplest original results (conjectures, rather) that have been pointed out to me.

Top statistical journals tend to publish papers with theoretical results, is that not a worry of yours?: I plan on getting an industry job after my Ph.D., so I'm not worrying about publishing in top journals.

Why not give up on your PhD title?: That would mean having to pay back tens of thousands of dollars that I received, and I don't have that money.

Related questions:

  1. What happens if a math PhD student fails to find a proof that is the main objective of his/her thesis topic?
  2. PhD theses in mathematics with no "big" results or no results at all
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    "having to pay back tens of thousands of dollars that I received," I don't understand. Are you receiving scholarship or TA ? Would you please explain ?
    – Nobody
    Aug 25, 2023 at 4:47
  • I'm afraid explaining that point would only help pinpoint my country or particular university. However, I can assure you the information provided in the question is accurate.
    – tZEugp915x
    Aug 25, 2023 at 13:43
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    You say that your background is not in statistics, but you are getting a PhD in it -- so, supposedly, your background will be in statistics. Do you not think that in itself merits the extra effort? Self doubt is a constant struggle during one's PhD but (I believe that) anything worth doing, is worth doing right. The right way here would be to build up to a place, through practice, from where you feel comfortable enough to take a chance on a proof. Sometimes they don't work out. That is just the reality of mathematics; but sometimes they do! Aug 25, 2023 at 14:51
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    I got a Master's before starting my PhD, and I also did not have faith in my ability to prove hard results. I will forever be grateful to my advisor at the time, who made me go through the process of just reading textbooks and working out the proofs. Sometimes we'd do it together on the board, and sometimes I would do it independently. At some point I realised that I had stopped fearing and learned to just love the process of thinking about a proof. If you really want a PhD, perhaps you should explore something similar with your advisor. Aug 25, 2023 at 14:56

2 Answers 2

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PhDs are assessed in different ways in different places, and even in the same place there is considerable variation between different reviewers. So a general answer can't be given.

That said, as a statistician I surely think that a PhD-worthy thesis can be produced without proving original theorems. A number of statistics PhD theses are applied; they may involve modelling and new methodological developments; these may not be groundbreaking but rather adapt things that already exist to the specific data to be analysed. Theory is not always (easily) available for methodological novelties, and quite a bit of methodological research is done via systematic simulations. A well done simulation study, a new methodological idea, and a competent discussion of existing literature can carry major weight of a PhD thesis. On top of that, in applied theses there can be issues such as handling missing values, and making sense of a large body of non-statistical literature that has implications on the analysis.

I have seen certain examples where PhD candidates do "theory for the sake of having some theory", and I didn't find this very convincing, even though it cannot denied that in this way a certain ability is demonstrated that some people expect. (These people probably are in Mathematics departments; elsewhere I don't think this is generally expected.)

Note also that some mathematical theory is rather easily available, for example showing whether an ad-hoc distance is actually a metric, i.e., fulfills the triangle inequality (it depends on the distance whether that's easy of course). I have seen a number of people introducing maximum likelihood estimators for some model, and one could show that these are asymptotically normal by just checking the assumptions of standard theory.

As a thesis reviewer I would criticise lack of theory if the thesis, in my view, "calls out" for such theory (e.g., asymptotic normality of the ML estimator is used but not proved, and neither in the literature for the specific instance), and I convince myself that the theory would have been easy enough to do. (If it's not there, I'd like to see a discussion and reasons why not.) But this only concerns specific theses, and would in most cases on its own not be a reason for failure anyway.

Note also that your situation, doing a statistics PhD without a very strong mathematical background, and lack of training and confidence with proving theorems, is not unusual. Many others have been in the same situation, and I believe that most of them eventually got their PhD.

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Is it possible? Certainly. I don't know how common it is, but (as an example) scrolling through the Statistics thesis repository at Carnegie Mellon University there are quite a few non-theory theses. For example, early in my time as a PhD student there, when I myself was nervous about my ability to publish theorems, I was heartened to watch the defense of Bronwyn Woods, whose thesis contributions "include the organization of existing resources, methodological advances in segmentation, motion correction and clustering, and the development of prototype analysis software." Very useful, important, and rigorous work, making substantial contributions to statistics -- just not primarily about theory.

Your PhD studies are a time for you to learn how to produce rigorous, original, publishable research in your field. And specifically in the field of Statistics, there are quite a few other ways to produce rigorous, original, publishable research beyond just proving theorems. So if you aren't keen to focus on proving theorems (which is fine!), you should aim for a thesis topic & advisor that let you practice producing the kinds of rigorous, original, publishable research you can feel confident about.

So I do suggest that you speak with your advisor.

  • In an ideal world, the advisor should be OK with the idea that a non-theory thesis could be a better fit for you. A good advisor should be happy to help you find a path along which you can make contributions to statistical research. (Even from a purely selfish perspective, they'd look better for advising a non-theoretician who goes on to be successful, vs for advising an unsuccessful theoretician.)
  • But if the advisor is adamant that your thesis must be mostly theorems, they are probably not the right advisor for you. (Not necessarily a bad advisor for others -- perhaps this advisor only works on theory and genuinely isn't qualified to advise a different kind of thesis.) Beware the sunk-cost fallacy; it may be better to take one step back & find a new advisor, instead of pushing further on a thesis you cannot complete. Is there someone else in your department who could (co-)advise you? Maybe another member of your committee, if you have one already?

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