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Is it acceptable to use working hours to read general interest books?
Every year are published around a dozen of books directly related to my research. By directly I mean on the same topics of my research, normally by authors I am familiar with, and potential reference sources for my current or future research. Some are fairly technical, but others can be of more general interest, or, for example, more on the policy side. I think it is not controversial that one uses "normal working hours" (e.g. whilst in the office) to read these books.
However, my problem is with books that are not directly related to my research, but which pertain to topics that might be indirectly related to it, or which I think might be interesting to explore in order to foster interdisciplinary work, or which are of another sub-discipline I don't research on, or books that refer to academia and science in general, or to teaching. Naturally, there are hundreds of these books published every year, and I do not aim to read them all. But some are of particular interest to me.
Is it socially acceptable for one to use "working hours" to read these books? Or are these expected to be read only outside work, e.g. on weekends, or holidays, or so? What's your experience on this?
Context: I'm a standard "early career researcher" in academia, with both research and teaching responsibilities.

You might want to clarify your position....if you are a tenured professor, I can't imagine why you care what anyone thinks, whereas if you are doing research in the private sector, the situation may be quite different.

@cag51 added...

Actually, @cag51, you don't even need to be tenured as long as you have a regular position in academia. You have a profession, not a job. You can ride a bicycle if it makes you more productive. I agree, though that private sector is different. Read Dilbert, for example.

Do you work a 40 hour work week?

@Buffy - yes, I was pointing out two extremes, not saying that only tenured professors can read in the office.

It’s a good thing Richard Feynman never cared about what was “accepted”, or about only using working hours to work on things “directly” related to his research.

@BryanKrause Yes, but strictly speaking, from a "philosophical" perspective, that should not matter. I might as well work 20 hours a week for my university, and 20 hours for the local restaurant. i still want to know if it is permissible to form myself in topics beyond my direct research using time paid by my university.

@luchonacho The reason it matters is that a typical professor-level academic doesn't really have working hours. They don't work 40 hours and then say they are done for the week. They likely don't track hours at all. That doesn't mean they can't have work-life balance, but whether it's okay to spend your 'work day' reading seems to me like it depends on how you define your work day in the first place.

@BryanKrause Yes, I agree that working hours are fuzzy when it comes to academia. But in my desire to balance family life with work, I do try to have a regular schedule of work. Actually, striking that balance is precisely the origin of my question. My wife complains that I want to read too much at home, for things that, according to her, are about "work" (e.g. not a novel), and thus should be done during working hours. These books are, as I said, "indirectly" related to my work. Hence my question of others' experience.

@luchonacho Then it depends on your life goals probably. Most people trying to get tenure probably aren't going to get there in a 40 hour week that includes teaching and outside reading. But no one is going to look over your shoulder and scold you for this, they will only critique your research output if it is not up to their expectations.

Related https://workplace.stackexchange.com/q/5602/25479

It's never acceptable to do anything when you should be doing something else. Should you be doing something else? I'll answer that for you: you should be not reading those books at home in front of the wife, that's for sure.

It's socially acceptable to do (almost) whatever you want during academic "free time". Ultimately what matters are the results you produce, and as long as you're able to do that most people will not care if you're reading general interest books, drinking coffee, or answering questions on StackExchange.
Of course, if you have assigned duties (e.g. teaching duties) then using that time to read books is not going to be permissible.

Thomas Edison was supposedly famous for taking naps during "working" hours.

@AliTaghavi no, academics are often not in their office, or even on campus, for all sorts of reasons.

@Buffy Thomas Edison was also famous for not sleeping, which probably explained the naps. "In an 1889 interview with Scientific American, Edison claimed he slept no more than four hours a day, and he apparently enforced the same vigilance among his employees." And expected his employees to do the same. I think most will agree sleeping regularly is better.

@Buffy: I don't know much about Edison's biography and have a rather naive picture of a self-employed inventor-entrepreneur in mind. Was he instead an academic in the sense that he received his salary from a university or similar?

@Schmuddi, see:https://en.wikipedia.org/wiki/Thomas_Edison. He was very productive, which was my point. It isn't hours at the desk that make you productive, but more than that.

@Buffy: But this question is not about productiveness, but about social acceptance of a certain behavior. If Edison's working environment and social position in the working place was drastically different from that of the average academic (i.e., the OP), then your point might be a bit moot.

@Schmuddi, yes, but I'm just commenting on the excellent answer of Allure.

@Schmuddi While one receives money with the name of a university on it, the money really starts and ends at the PI. So really, it's what's acceptable to him or her. University and small business/self-employed are not far apart in some aspects.

@Allure My example of "in the office" was not really to be taken literally. It was really meant to mean "time paid by my employer".

The main problem with this answer is that it relies on an undefined "result" concept. What is that? Publications? Teaching quality? Research impact? Personal development? Student development? All the above?

@luchonacho whatever it is that gets you promoted.

General workplace answer goes something like:

Ask your manager, and be ready to argument for whatever you plan to do. But it is manager's prerogative to specify and prioritize your workplace activities.

Academia is a special case of workplace, so asking is always a good idea. I imagine that worst case scenario would be your boss saying:

Sure, not problem, as long as that doesn't interfere with your responsibilities. And maybe not at your desk, so that people passing by have no desire to stop and chat about the book [in case of open space for example - aaaa]

On the other hand, academia is a special case of workplace so I doubt anyone would care if you read a book even at your desk. Even if it is not directly or even tangentially related to your work. As long as you fulfill your duties.

"As long as you fulfill your duties." So, if being a great lecturer is part of my duties (which I think it is), then I should definitely be alright in using my paid time for improving my teaching skills, e.g. through books on education. The problem is that, in my experience, few really care about being a good teacher. Most care about publications.

@luchonacho the students care. What they think will reflect in course evaluations.

Research is such a variable area that you never know WHAT is coming down the road next.  You may not need something right now, but spending some time trying to maintain a big picture when your own work is a tightly cropped photo often pays off.  Reading stuff outside your own area will make you a better researcher.
If you're worried about the time you spend doing it, budget that time.  For example, set aside a few hours a week for such a purpose.  If you think it's important, don't skip this time.  Conversely, if you think about 2 hours a week is right, don't spend three or four.

You mentioned that your responsibilities include teaching as well as research. A good teacher will often go off on brief tangents designed to raise student's interest (although a bad teacher will get lost there). An example from my own field (mathematics) is that I regularly teach a course in cryptography. Almost everything in classical cryptography is technically irrelevant in modern cryptography. Nevertheless, whenever I teach cryptography, I sprinkle my lectures with tidbits from military history involving code breaking, including lesser known ones such as the breaking of the German ADFGX cipher in 1918 and its role in halting the German Spring Offensive. The only way I know about such things is that I read about them, sometimes even in semesters during which I am not currently teaching such a course, in books which are only tangentially related to my discipline. I've never felt it inappropriate to read such books in my office but instead leave them scattered around in plain sight. A certain amount of outside reading is not only appropriate, but is indeed almost essential if you are to be a well-rounded teacher.

It is perfectly okay to read books at work as long as you are able to do your assigned duties. Once George R.R. Martin said,

A reader lives a thousand lives before he dies . . . The man who never reads lives only one.

I have personally felt it. Reading books gives you the imagination and power of thinking out of the box. You would always find top class CEOs or CxOs reading books during their free time. Reading is essential for those who seek to rise above the ordinary. Reading your interesting books also provide you overall satisfaction and personal experience which ultimately increases your performance and attention at the job. Moreover, long story in short, The man who does not read good books is no better than the man who can’t. Keep reading and be happy.
Amen!

Why Down voted? Can you please enlighten me?

Not the down-voter, but I suspect it's because this addresses how people should react, rather than gauging how an average person would react.

I didn't downvote either, but the "do what provides overall satisfaction and personal experience" statement is a bit naive. There are several things that provide overall satisfaction and personal experience which are unlikely to increase my performance and attention as an academic, and which rightly have no place at the job. Also, the last sentence seems to refer to personal growth, which may be a laudable life goal, but which is not what people are generally paid for, not even at universities. In sum, this answer is a bit too simplistic for my taste, and perhaps also for the downvoter.

Welcome to Academia@SE! The question asks about reading in the office and your point (however valid) is about reading in general. This page might help you in writing a good answer https://academia.stackexchange.com/help/how-to-answer

Thanks to all of you. I would definitely try to follow guidelines to be very specific.

FWIW: The "thousand lives" quote is a somewhat de-cluttered version of one by Umberto Eco. Jojen Reed was a reader :)

In the case of GRRM’s books the quote should more accurately say, “A reader dies a thousand deaths …”

In the context of this question, another ASOIAF quote appears just as pertinent: "A dragon is no slave."


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Are there any telltale signs that an academic job interview went well (or badly)?
I am currently in the agonizing period shortly after an academic job interview at a regional research school and I'm trying to analyze the day to estimate my chances of getting an offer.
Overall the interview seemed to go well. The talk was fine and the one on one interviews and meals seemed to go smoothly. But, I wonder how much of that has to do with my interviewers just being nice people.
Are there any people here with experience - on either side of the interview process - who can provide any feedback about how the interviewers might behave before they do (or don't) ultimately extend the offer?
If it's relevant, I happen to know I was the last of four finalists invited to campus and it's been at least two weeks since another candidate was interviewed.

In my experience interviews/job talks cannot go well, they can only go poorly. In other words it is hard to move up the short list but relatively easy to move down it.

If your goal is to get information from us and use that to try to figure out whether you will get the job offer: I'm sorry to tell you that's just not going to be possible, with any degree of accuracy.  I understand the impulse to analyze and process such an event, but I honestly don't think that impulse is a helpful one in this situation.  Wondering whether you'll get the job does not actually do anything positive for you: it's just wasted mental and emotional energy.  Try doing something more relaxing and/or more productive instead.

If you get a job offer it went well enough. If you don't, it didn't.

Hi @Pete, and thanks for your attention. My goal is to find out exactly what I wrote in the question: has anyone observed any particular interviewer behaviors that indicate which way they are leaning (e.g. The level of detail the dean/chair goes into wrt tenure requirements or how casually they treat you or the language they use when you say goodbye)? I thought this may be of interest to others in the future, and no such information is easily found on the internet (wrt academic jobs at least), so I asked the community. It sounds like your answer is "No, I have not". Good to know. Thanks.

If I or some other experienced academic sat down with N job candidates post-interview and interviewed them in detail to inquire how their interviews went, then I would expect that my average success rate at guessing whether they will receive an offer would be higher than theirs, probably to a statistically significant amount for sufficiently large N.  However my success rate would be so much less than 100% that it would feel irresponsible of me to present my opinion to them under the guise of expertise.

In view of this, my best advice really is to try to relax and/or keep busy with other work.  All advice has a "take it or leave it" quality; you are of course absolutely free to leave it.

@EnergyNumbers I totally disagree with "If you don't, it didn't". Quite often a candidate will rock the interview and another will bomb it and the one who bombed it will still get the offer.

Ok @Pete, thank you for your view. Your suggestion to not bother worrying about it is well taken. I guess my target with this question was to find out things like "If ______ happens, this is a bad sign or if your interviewer says _______, this is a good sign", which I believe would be of general interest to future readers. Your experience seems to be that no such general statements can be made with anything in the realm of certainty which, in itself, will be of interest to future readers. Again, thanks for your attention.

If they want to talk to you after the interview, but before the offer letter arrives, maybe that's a good sign.

Even if a department loves you, it's unlikely they'll show any overt signs (especially in an official capacity) because they want to maintain a reasonable negotiating position.
But individual faculty are not always "with the program". For example, it's happened to me that someone said (during an interview) "I really like your profile and will be making a strong case to make you an offer". But while this might be flattering, it also depends on how much power that individual has over final decisions.

Alternately, many departments will show overt signs of liking all the candidates they interview.  For job searches in which three or four people are picked out of a pool of hundreds, this seems appropriate, and it may be a better recruitment strategy than remaining aloof.  Anyway, the point is you really can't tell.

I can only speak from experience: I was first out of four candidates and I was given great informal feedback by the search committee chair (i.e. "You set the bar high"). I came in second as their "alternate choice" and did not get the job...

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When should you not report plagiarism?
It seems that most of the ethics and plagiarism questions follow the same basic archetype:

Q: I think there's some plagiarism going on, and I don't really want to report it, but I think I should. Should I?
A: It'll be tricky, but yes, of course you should report it. Consult your institution for more details.

My question: is there any case of academic dishonesty (including plagiarism) that a student/TA should almost certainly not report?

Are you asking when one should refrain from reporting misconduct for ethical reasons? For selfish reasons? Or both?

@ff524 both, though I guess selfish reasons basically don't count.

When you're not sure is probably one situation. You're accusing someone of dishonesty so you'd better be able to prove it.

It's not really up to a student "informer" to prove anything. All you can (or should) do if you're reporting possible plagiarism, is describe what you have seen that you think the instructor hasn't, in a non-judgmental and objective way.

@ff524: I don't know about that, I would be more inclined to keep my mouth shut if I wasn't too sure something is plagiarism. At the very least you risk your reliability as a signal source (aka if you're wrong then people are less likely to care about your thoughts later), and in the worst case the other person is actually innocent and finds out and becomes offended, ruining your relationship in the process.

@Mehrdad - Yes, but in maths' language, that's the ''trivial solution''. You are right, when OP isn't sure, he certainly should keep quiet. But when he uses the words ''academic dishonesty'' in an in-principle type of a question, isn't it taken for granted that he has reasons to be sure? Then, he's looking for ''non-trivial solutions''.

@ff524 - But if you don't put the requirement of proving the claims, won't there be too many false, or misguided claims? And all that whoever-is=the-judge will do in a day's work is to attend to frivolous dishonesty complaints.

@New_new_newbie I don't see why this should create frivolous claims, if everyone acts in good faith. And regardless, it's not a student's job to judge other students; presumably the faculty/adminstration is better equipped to investigate a claim (collect evidence, speak to the student directly, etc.) anyways.

@ff524 - Because everyone won't act in good faith. What if the claims are used to extract ''revenge'' for a bad viva-voce? You are right, the admins are better placed to judge, but they can certainly reduce their workload if they start asking for some evidence. Or at least, a ''how do you know ?'' Otherwise, this is all that they will do in a day's work.

@New_new_newbie I don't really see malicious accusations of dishonesty as such a common problem that administrators will become overwhelmed by the workload. Perhaps you and I come from different academic cultures...

@ff524 - It was an in-principle response to an in-principle type of a question. Thankfully, claims of dishonesty aren't that rampant in my ''academic culture'', though a sourcing requirement/ supporting evidence for anything that one claims is. Let's leave it at this point, this discussion is becoming ''an extended discussion'' (move-to-chat-type) now. Cheers :)

won't there be too many false, or misguided claims? — Perhaps in principle, but not in practice.

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Relational Reference Management Software?
I'm looking for a reference management program to do three things:
(1) Manage references. There are plenty of solutions for this.
(2) Manage quotations. Similar to this question, whose suggestions were surprisingly lacking.
(3) Map relationships among references. Somewhat similar to the problem posed by this question. When doing research, I often (naturally) find new sources via reference from a current source. It would be extremely useful if my reference manager could look at all sources referenced by all of my current sources to reveal data gold mines like which sources in my library are referenced by other sources in my library, or (even better) what sources are referenced most frequently by all of the sources in my library.
For instance, if my library/collection contains sources A, B, C, D, E, then I want to see that A references B & E, B references D & E, and C references E. This information would also lend itself to useful descriptive statistics like "E is referenced by 3 sources in your library {A,B,C}" and even visual mapping of references.
With the vast array of reference management software available, I am amazed that I haven't found anything even close to this. Does anything like this currently exist?

[softwarerecs.se]

As far as I know, there is nothing pre-canned for point (3). If you want to do it yourself, you'll need:

a way to retrieve automatically the references cited in each paper of your collection -- see here for some solutions. Note that an important part of this is giving each paper a unique identifier, something that is dealt with in literature databases.
a graph visualization/analysis tool. See for instance here.
Writing code -- sorry, there is no way around this. :(

EDIT: just noticed that this answer suggests a program called Google Scholar Citation Visualisation Tool -- give it a try!


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Considering a second PhD, this time in statistics. Will any professors be interested in working with me?
I have a PhD in applied mathematics from a top university. I didn't realise it at the time because I was so focussed on my research, but it seems my research area (numerical analysis of PDEs) is actually very niche in terms of real-world problems in industry.
All the job listings I see are looking for people with a probability/statistics/machine learning background. There are literally hundreds of listings for these types of jobs for every one job in my area. I am currently a postdoc but I plan to leave academia for industry (probably finance) in the near future.
As the need for people with statistical skills is so clear, I have started considering the possibility of doing a second PhD, this time in a statistical area.
It would have to be part-time as I will be working full-time in industry. However, I currently work 7 days a week and very long hours on research as a postdoc, it is my hobby as much as my job. So when I begin working in industry, which is a standard 40 hour Mon-Fri working week, from my perspective I will have a huge amount of free time, i.e. eveninings and weekends, that I could dedicate to statistical research.
So I have several motivating factors for considering doing a second PhD:

Statistical skills are very much in demand and are of far more use in real-world applications than my own niche skillset. I would like to work on important real-world problems and it would be ideal to have a qualification for this.
I absolutely love research and having a goal to work towards to motivate me to get really deep into a topic. I will have alot of free time when I move to industry soon, and I would like to dedicate that time to something constructive.
I am interested in probability and statistics from a theoretical perspective and I would like to make a contribution to this field. Originally I applied for PhDs in this area, along with applied math PhDs; I went with the math PhD as I was also very interested in that area and it was a great opportunity to work with an excellent supervisor and university.

So a second PhD in a statistical area seems to make alot of sense to me.
Questions:

Would any professor even be interested in working with me considering
that I already a PhD and that I plan to work on the second PhD during evenings and weekends?
If you think there are professors that might be interested, how
should I go about approaching them? I would not like to appear
unprofessional.
I don't need any funding as I will be working full-time. Will this help me in finding a professor for a second PhD or is it largely irrelevant?

A PhD certifies you can do research. It is not an efficient way of certifying that you have knowledge of a particular field (that was usually a precondition before entering the program). So a second PhD is not going to help you. What you need is to fill the gaps in your knowledge and get some work experience. Probably the most efficient way to fill the knowledge gap is to join some MOOCs.

@Roland I already am a postdoc and when my contract ends I will be leaving for a job in industry. But I still want to perform research on the evenings/weekends as it's my hobby as much as my job and I love it. As I aim to move into the area of statistical analysis I would like a qualification in this area. I can't simply stay in academia and switch my research..nobody will hire me for a postdoc involving stats/probability, they will hire someone with much more expertise in that area. I also have financial commitments which mean I cannot afford to do another postdoc.

@MaartenBuis It seems to me that a PhD does certify that you have deep knowledge of a particular field. Anyone I know who completed a PhD in mathematics is far more knowledgeable about their field at the end of the PhD than they were at the start. The precondition for entry in mathematics is that you have potential based on your prior academic achievements.

Of course you know more about the subject of your thesis after writing it than before. That is not the point, because your research is in all likelihood way to narrow and specific to justify hiring you for a substantial amount of time. The real value of your diploma is that it shows you can do this again, and again, and again on different topics.

@MaartenBuis Yes and I would very much like to do it again in the area of statistical analysis. What else am I going to do in the evenings/weekends..watch TV/go drinking with friends/computer games? I want to continue my current lifestyle when I leave academia for industry..I want to perform research on the evenings/weekends, and publish papers. Longer term, I want a career in the area of statistical analysis and I would like to be highly qualified for this. My own area feels too niche and of little relevance to the real world.

If I move into research in this area I will gain far more proficiency if it is as part of a structured cutting edge research project with clear goals and I am working with a professor who has years of experience. At the end of the process I will have a qualification that certifies my expertise in this area. I would be in a far better position than I currently am - there are countless jobs that I am very much interested in but I am not eligible for because they require probability or statistics PhDs.

@SecondPhD - you seem to be of the opinion that you need to go do another PhD if what you are working on isn't quite what you did your first PhD on. I would suggest that most PhDs are doing something fairly different within 5-10 years of their thesis, yet do not feel they need to get another PhD - they learned how to learn new stuff on the fly without courses in the first one.

I agree with @MaartenBuis - the first PhD should have given you the skills needed to do independent research. You don't need to relearn that with a second PhD, and moreover, you should be able to do independent research in the field of your proposed second PhD without the need for a PhD advisor. You'll want to be in touch with some domain experts, but you should just focus on research rather than "learning to research,"  which is no small part of a PhD.

"my research area (numerical analysis of PDEs) is actually very niche in terms of real-world problems in industry." Very strange. I know people in industry who have spent literally decades working on that topic. Maybe you aren't talking to the right companies.

Relevant Meta:  https://academia.meta.stackexchange.com/questions/4623/contesting-a-duplicate-question-non-standard-career-path-and-two-phds

@alephzero Yes there are some jobs in this area it is definitely a niche area these days compared to the amount of positions that look for a statistical skillset. Practically every decent sized company/institution is looking for a statistical skillset now.

I have realised based on the answers I've received that my math PhD will be viewed as essentially equivalent to a stats/ML PhD in industry so I can see now that there is no need to do a second PhD in this area.

Here, in Germany or at least at my former university, it is not possible to receive two PhDs. If one wants, they have to be done in different fields. Let's say someone earns a Phd in chemistry then you would get a "Dr. rer. nat." like everybody did in nature sciences (biology, chemistry, physics and also mathematics,.. etc.). So the 2nd would have to be done in.. Idk.. history or so..
but btw, here you would have no problem to get a job in statistics/mal even when you're not from that field. Mathematics is sufficient. And I doubt a phd in statistics would help

as it is very specialized. My background is experimental particle physics and I'm working in a machine learning job now. Before that I worked with the statistics lecturer (a mathematician) and the skills needed in the industry are very different. You need much more software and programming than statistics/ml. At least on a daily basis.
Nevertheless, I feel that there are a lot of statisticial questions during the week and they are so very applied that I think one can only answer them thanks to experience and/or as studying statistics (but not as a phd..).

If you want to do research in statistics, why not doing just that? You can publish without being part of a university and you can use your free time for actual research and not the things which are attached to being a PhD student.

@Ben That's interesting. How did you mange to hired for a machine learning job ahead of statistics/ML people when your background is experimental particle physics?

@SecondPhD Probably because of two aspects: Though I'm part of a relative large AI team (around 25 people) but finally I'm part of a small team embedded in an engineering department (here I benefit from my experience in particle physics, especially as my work included a lot of technical details and also data analysis of particle experiments) and, as I just mentioned, my work was related to a lot of statistical questions. I had no clue about machine learning but I'm, at least, familiar with statistics and programming.

Finally, I would say, statistics is a field of a lot of applied tasks - and I think, a phd would focus on "only" improving some optimization tasks behind some questions, so rather mathematically. So I guess, in a "typical" data science/ml job you wouldn't really benefit from that. On the other side, there are, for sure, jobs which are very suited for such tasks, where you would work on implementations and so on and less on "answering applied questions". The former would be very mathematically while the last would be more applied statistics, programming and so on.

However, my (small) team consists of: A mechatronic engineer, an electrical engineer, two mathematicians (one of them is my boss) and me.
A company near to us is developing concepts and pipelines for other companies and they only hire mathematicians and physicists. They told me that, as a data scientist, you wouldn't almost need any programming as you pass your (mathematical) models on to (pure) programmers which convert the models into software later. I guess, e.g., this would be a perfect job for you - while I feel more comfortable around technical guys :)

Just a few things to consider: it might not always be that the hours you have to put in the job in finance industry are limited by 40h per week. Also, maybe you are the exception, but a lot of Phd students who think they work 7 days per week are actually surprised about the high pace of industry work. Your first priority should be to survive the first year on the job well.

I used to have a job as a quant in a top hedge fund and I only had a masters. I actually left it for a phd in machine learning. You definitely don't need a different phd. you're perfectly well qualified. Just pick up some books on machine learning/general finance (e.g. bishops book and grinold & kahn) and you''ll be fine.

As most of the comments already say, doing a second PhD is probably not a good use of your time and not very helpful. From an industry perspective you have a math PhD, that means you are very smart and can learn anything, especially any maths very quickly. Learning some statistics or machine learning can be very helpful for an industry career but there is no need to get a formal degree in it. You can state in job applications that you have a math PhD and that you know statistics. Even if the statistics is entirely self taught in the real industry world this is equivalent to a PhD in statistics.

This.  If I ever saw an applicant who returned to school for a second PhD, especially in such a closely related field, it would seriously devalue both degrees - because even after completing one, the person still seemed incapable of independently teaching themselves something new.  That's not a good sign.  Going back to training wheels is not a step forward.

"Learning some statistics or machine learning can be very helpful for an industry career but there is no need to get a formal degree in it." As someone who reviews applicants for data scientists, I have to strongly disagree with this. There is plenty of work that someone without formal training in Stats/ML can do, but there's also some work that just can't be. There are plenty of stats related positions that are open if you don't have formal training, but some that are definitively closed.

My Ph.D. was in discrete optimization, I was hired for an industry research position in statistics/time series analysis/forecasting, I have been working in this job for 14 years, and I endorse this message. +1.

Ok this puts my mind at rest. I thought I had no chance at these jobs compared to stats/ML people but if you say my PhD will be viewed as essentially equivalent in industry then I should be fine. I was concerned I had pigeonholed myself into a niche area.

@CliffAB As someone who has transferred into data science from a physics background I disagree with the statement that some can't be done without formal training. I'm not saying its easy but there are plenty of resources out there to learn if you want to. The OP has shown they can learn independently in a related field, I wouldn't have reason to doubt they can do so without another degree/PhD. I believe the idea that anything is 'closed' without formal training is flawed.

@LioElbammalf: where I work, much of the role involves advising other non-stats PhDs on statistical methods. This requires a lot of breadth of knowledge. I've interviewed a lot of physics PhDs that understand a particular area of stats/ML well, but if you ask them something outside that area they have no idea. Hard to do statistical advising if that's your background.

From your question and comments it seems that there are two distinct issues:

Finding a good industry job with your profile: since you mention statistics I assume that you are looking at jobs in the area of data science. There is so much demand in this area that you don't even need a formal diploma, you could just teach yourself and acquire some experience with online material.  In case you really want a diploma, what you need is a Master, not a PhD.
Keeping doing research because you enjoy it: you don't need to do another PhD in order to keep practicing research as a hobby. Most advisors would be reluctant to
take you on anyway if you are not able to work full time on the PhD: they would often see you as a "flight risk", since you don't have any specific funding and no strong reason to finish a (second) PhD. However you could certainly collaborate on some research projects, many academics would appreciate a skilled collaborator that they don't have to pay.

Thanks for this advice, this sound like I a great idea. I will attempt to find an academic with experience in this area so I can collaborate while transitioning into this area.

I'm a retired Uni academic.
If you are determined to study more (and bravo for that), I would strongly recommend an MSc or an MMath in the subject. If you are short on skills, you will get far more from a good Masters. It will be cheaper, quicker and more intensive. Frankly, you will learn more as well. You've proved you can do research and that's the end of PhD for you. You would slow down your career too much by doing another.
Distance learning is appropriate if you are strapped for time.

I also have a PhD in numerical analysis of PDEs (I would be interested to know in more detail what you studied).
During my studies I made good friends with a senior statistics professor at the university and I was pretty surprised by the amount of overlap some of the things we were looking at. In particular we had a good conversation about applications of the Proper Orthogonal Decomposition which had applications in my particular studies (reduced basis approximations of high dimensions PDEs) as well as in simplifying complex statistical models.
There is also an obvious huge overlap if you ever worked in the area of stochastic PDEs. I feel like I could have definitely continued a post doctorate with this statistics professor if he had anything available and I actually wanted to. I would suggest doing some background research into what some specific statistics professors have been working on and see if you can tie any of it back into your own studies. I think you'll be surprised.
On a side note, I didn't want to continue in academia and instead became a software developer. If you have some computational experience, which I assume you will having done numerical analysis, then I would also recommend looking into this because it is also in huge demand at the moment and it's really fun. ;)

More properly, you did your PhD research on a certain topic.  You were awarded your PhD in some larger field, like Math or Comp Sci.

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Master’s program rejection with no reason. How to appeal?
I recently applied for a master’s program and I fulfill all their requirements. In addition, the application fee was around 100 USD that had to be paid in advance. I got the following answer today:

I would like to thank you for considering XXX for your graduate study. We have very
  carefully reviewed and evaluated your application to the Master of Science in YYY program for the Fall 2020 semester and I regret to inform you that the
  Admission Committee has declined your application for admission.
  We hope that you will pursue all of your other options for continuing your education and
  achieve academic success in another graduate program.
  Best wishes for success in achieving your academic and professional goals.
  Sincerely,

Is this normal that they do not provide at least a very brief explanation on why they refuse my application, since I fulfilled all the requirements?
Now, how to appeal, i.e. I do not know why they refuse me so I do not know what I should tell them.

What do you really hope your appeal would accomplish? They've probably filled all their spots already.

Given that universities in many countries do not simply admit students (esp for graduate level) on a "checkbox" basis: which country is this and are universities there expected to admit all Master students who fulfil the minimum requirements?

Minimum requirements are just that: minimum. They determine who will not be considered for admission, not who will be admitted.

You guys did not get the point of the question. Is not to get admitted, is to know why I did not get admitted.

Yes, that's totally normal. For example, my department typically gets 150-200 applications for the PhD program every spring, but we only accept ~20 students. That is a process that is exceedingly time consuming already as is. It is simply not reasonable to write up an explanation why the other 130-180 students were not accepted.
That's no different than for most job applications: The company you sent your application to will simply say "Sorry, we didn't choose you because we found someone who is better qualified", rather than giving you feedback where exactly they found your application lacking.
As for appeal -- I don't think any university has a process for this, but pretend for a moment that there were: What do you think would happen? The university made a determination that you were not as well qualified as someone else. You can appeal a decision if you think that a process was not followed, e.g., if your application had arrived in time but was rejected because the department (erroneously) claimed that it was late. But your question does not contain any indication that you have any evidence to this effect. On the other hand, you can't appeal a rejection because you weren't as well qualified as some other candidate -- your "appeal" in that case ought to be to get more experience and more education so that on your next try, you are better qualified.

There likely is no appeals process, and if there was, it would likely not work out in your favor unless there were some major error (on their part) with the handling of your application package.
In short, they don't owe explanation.  In fact, you might be every bit as qualified as applicants who were accepted, and they flipped a coin to avoid overloading their program with students.
Of course, things are different if you feel you were not offered admission based upon some illegal decision making process, such as discrimination (in some countries), but then your process would involve lawyers.
Your best course of action is probably to speak with a trusted mentor about how to make your application package look more attractive.
There is also the possibility that your application package is simply not competitive, and you might need to adjust your expectations (we have no way of knowing, but the possibility should certainly be considered).

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Collaboration tool for students and teachers
I'm looking for a tool (web application) similar to Atlassian Confluence but for academic purposes. The main purpose of that is to allow selected teachers and students from different schools collaborate on a project. It should have features like file uploading, to-do lists, calendar, rules assigning etc. Also it should have a possible low price-tag. I'm from Europe so if it could be a European tool that would be great.

What features are you looking for that Confluence doesn't have? How big teams?

Confluence has everything we need and much much more. The problem is that it is probably too powerful and not created especially to schools. There will be about 60 people in our team. I have never read of a school which used Confluence, but I believe that they must be using something to get their work done.

I understand you mean "school" as in "before university", so you are working with non professional researchers. In this case, I think the most important bit is to keep it simple, and use tools people know and are familiar with and just work. So,

Mailing list: for group-wide communication. There are tons of free alternatives.
File upload: shared Dropbox folder or ownCloud. With the extra benefit that everybody has a backup of everything, so nothing gets lost.
Everything else: any free wiki.

That should cover all your needs.

Probably you want to look into JiveOn software.  It is mostly used by businesses, but that groups includes Google and Pearson (a US education supplier).  I have used it in a corporate setting, and can speak well of its ability to create collaboration spaces, and reinforce a group-learning social network.
If you find they have pricing for education, or a course-long trial period, it would be good to hear.  Seeing their potential, I would not be surprised that you could negotiate a deal, though as a EU-based learning org, you may not be able to offer them US-based tax deferral (501c3).  Maybe they have a EU arm.

You should find this wiki comparison engine helpful:
http://www.wikimatrix.org/
I've personally seen wikis used very successfully in large academic environments.
A couple of suggestions thought:

Don't think that because you start a wiki, people will use it. You must do most of the work initially, placing the content in there, structuring the content, etc. Others will come and contribute on their own, but only once you've seeded 90% of the content.
Do not lock down the wiki with too many rules. As long as you lock out spammers and strangers, that should be enough, give everyone the same editing privileges on all the pages. You're not wikipedia.
Make refactoring a wiki page a homework assignment for newcomers. Newcomers to wikis are afraid of refactoring existing wiki pages, for fear of deleting other people's content. Making refactoring a wiki page a requirement will alleviate that fear.
Make editing the wiki an homework assignment for newcomers. Ask them to create a profile for themselves as a wiki page. Ask them to use their real names when registering, or at least their real first name (unless they're too young obviously).
Make sure that the wiki has a RecentPages functionality and bookmark that page. That will be your primary way of observing what's happening in your wiki.


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Is it okay to work in different lab during Summer?
I was wondering if it is okay to work in different lab during summer.
I am first year master's student and currently I am working in the lab as volunteer at my college. During summer (May through August), because of housing/financial reasons, I have to go back home which is in different state. Despite wasting my summer without doing anything, I want to work in the lab in the different college that is working on similar project and the lab that is located near my home. Can I do that? I guess I care about if it would be rude or not. I will definitely tell my current P.I. and potential summer P.I. about it but I wanted to make sure it's okay before I proceed.

Ask your advisor.
Whether it is okay or not will depend entirely on him. He may have some reason why he wouldn't want you to (rational or not). It shouldn't come across as rude if you ask him openly and express your reasoning (have to go back home but would still like to be research productive and may be a good opportunity to network with the other lab).
Whether the other lab will let you, is a different question entirely. Another option is working remotely with your current lab.

It depends on funding.
If your advisor at your master's program is providing you with a year-round stipend, then you should secure permission in advance, as suggested by Austin Henley.
On the other hand, if you are in fact volunteering in the laboratory as part of your master's program (or are doing it for course credit), and are not receiving either a salary or stipend for your work during the term, then your advisor really does not have the right to control your work during the summer if classes are not in session.
In any event, you should let your advisor know about your situation, as your PI may have advice or recommendations. However, this is purely informative.


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Mentioning a published paper from a current team member in a PhD letter application
I am writing a motivation letter for a PhD position related to antibody analytics. While I was researching the current research group I read a paper published by current team member "A" which I found pretty interesting, and honestly, also quite motivating.
I would like to include this in my motivation letter, but the problem is that "A" published this paper when she was working at another research institute. NOT the one I would be currently applying to.
Would it be a faux pas to mention this paper in my motivation letter?. On the one side it would show that I have actually researched them, and that I am actually motivated (which I explain why). On the other side, it could give the impression that I thought it was published by them, and I was not careful.

I think the content of the publication is more important than the author of the publication.  I would mention the paper as well as other similar papers that you have read that are by other authors.  Most likely, your  person "A" will know of the other authors in your field so it is not necessary to soley focus on mentioning person "A."  I think it is more important to show your interest in the specific research area rather than knowing where person "A" worked.

Should I mention the paper like this? "when I read 'xxxx' published by current team member 'A' I became thrilled about blablabla..."

I would look at the conclusions and future work portion of the publication.   The papers typically have a section at the end that lists improvements and future research gaps that need filled.   In your application, write how you are interested in pursuing one of the future research topics they listed.  Explain how you might want to contribute.  Explain how you can assist them in their research by prototyping, running simulations, etc.
You need to convey that you understand the research process and would be of use on their team.  Good Luck.


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Do I need to take the TOEFL Exam?
I studied in the US up until the 6th grade, came back to India, completed high school here and finished a bachelor degree in computer science here in India. All my education in India was completed in English only. I am also a US citizen. I want to pursue a masters degree in the US. Do I need to take the TOEFL exam or not?

Yes, it is a good option, to prevent academic hassles.

I know a guy that was asked to take the exam for a Postdoc after he completed a Masters and a PhD in a British university with 4 journal publications. Sometimes the "need" is not for proving your English skill, its  just some legal paperwork you need to go trough. Just take it.

The safest course of action would just be to take it.
Each department at each university has its own policy on the TOEFL.  Usually international students from non-English speaking countries are required to take it, but there may be exceptions.  It is common for there to be an exception for students who have attended a university in the US, but that does not apply to you.
So you'd have to check the policies of each department where you want to apply, to see if they have an exception that does apply to you.  Or, if there is no written exception that applies, you could contact them directly, explain your situation, and ask them to waive the requirement (of course there is no guarantee that they will agree).
So unless you can verify that every department you care about will accept you without a TOEFL score, you had better take the exam.

Most schools will say that they don't require the TOEFL if the language of instruction in your previous institution was English.
In reality, if you're applying from outside USA/England/Australia/New Zealand, then it's a very good idea to have TOEFLs (unless your GRE verbal scores are very high - top 10 percentile).
You want to extinguish any possible doubt about your English-speaking ability.

The first few programs I checked had wording like "we don't require the TOEFL if you previously attended an English-speaking university in an English-speaking country".

English is one of the official languages of the Union of India, along with 22 others.... Given that most people don't know that, it's safe to take the TOEFL without pushing the issue too much.

Absolutely concur: take it, ace it, and remove a possible hassle from your future.  And if you can't easily ace it... well... you shouldn't try to wish that away either.


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Which GPA is more important for applying to PhD programs, graduate or undergraduate?
I have BE and MS from the University of Tokyo (a research university in Japan) and
I'm planning to study and do research in US as a PhD student.
My questions is: which is more important, GPA at graduate school or GPA at undergraduate school?
I have a GPA of 3.8ish at graduate school and a GPA of 3.4ish at undergraduate school.
So this is a very important problem to me.

In general, more recent grades are considered more relevant.  But in the US, Master's and PhD are often obtained as part of a single continuous program, so many applicants to PhD programs do not have any graduate GPA at all.

One data point: My undergrad GPA was 1.5 less than my masters GPA (both out of 4.0) and I got into a top PhD program.

I think the answer is very geographically dependent. If, as brenbarn and Roger suggest, you are applying in the US, the undergraduate grades will be used as a means of comparison, since that's the common "core" that all US students will have. Your additional experience will also be taken into account, and can be useful, particularly if it goes beyond coursework and encompasses research.
If you are applying in most of the rest of the world, where a master's is expected before beginning PhD studies, then I think the rough "pecking order" in terms of importance is given to:

Core graduate classes
Core undergraduate classes
Elective courses in your major
Courses outside your major

So courses early on (especially in subjects outside your "main" discipline) will automatically weigh less in an evaluation. Exactly how much less is impossible to say, but most graduate schools will be willing to overlook one or two weak grades that don't directly pertain to your PhD program. A poor grade in a master's level "core" subject, however, would be very damaging to your chances. (As a chemical engineer, I would be reluctant to admit to a PhD program someone who struggled in thermodynamics, for instance.)

There are several competing concerns that make this question difficult to answer.
As BrenBarn notes in his comment, more recent information is more indicative of your current skills. Especially if you show significant improvement, which most programs will take into account. Graduate programs will also (hopefully) consist of more difficult classes that are closer to the difficulty level of the material you will be tackling in graduate school.
On the other hand, applications seem to be very comparison-based. Applicants for a year are compared against each other and also against successful (or unsuccessful) candidates from previous years. This is anecdotal, but I know that some programs explicitly group and rank candidates based on their undergraduate institution for their first round of decisions (e.g. the top couple from each top-5 school are always accepted, the bottom third from most schools are rejected). The fact that most applicants to US PhD programs don't have a graduate degree means that your undergraduate GPA may be more convenient for these comparisons, so it might actually get looked at more.
In programs that do this, I suspect that your undergraduate GPA will be more important for getting into the group of applicants that will actually be considered, but that your graduate GPA will become more important after that, as the committee examines the borderline applications more closely. But this is probably dependent on the specific program and their procedures, and I don't think that there is a consistent answer.

Every university is different in this regard, so it is a good idea to just ask the admissions department directly.  I did this when working on my application, as I had a much higher graduate GPA than undergrad.  In my case, I was told that they didn't care about the undergrad grades at all, and would only look at graduate (these were urban planning departments at top-tier universities in the U.S.).
However, it might be different if your PhD field matches more closely to your undergraduate field and not your graduate field.  It may also make a difference if your graduate work was in another country so that it is harder to compare grades or at a less respected institution.
So, don't be afraid to just ask!  But contact admissions, not any of the professors, since they (1) don't usually make those kinds of decisions and (2) wouldn't appreciate being bothered for this type of inquiry.


text:
How do I referee a paper when the authors may be behaving dishonestly?
This is a follow-up to this question:  Should I recommend rejection for a paper I referee if the authors don't make changes that they could have made?
To recap:  I was sent a paper to referee by a top journal.  My first report pointed out that the authors had only considered one of two possible behavior patterns that their system might have.  When the authors resubmitted, they basically ignored what I had said (although see below).  So I sent it back, asking that the changes I had asked for before actually be made.
When I read the second draft very carefully, I saw they had actually added a very short paragraph that seemed to be arguing against the second type of behavior I was worried about being possible.  It was so minor I didn't even notice it before I asked my earlier StackExchange question.  However, the manuscript authors made an error in their attempt to demonstrate this point quantitatively; as I pointed out in my second report, their argument actually showed that I was correct.
Now, they have submitted the paper a third time.  This time, they very enthusiastically acknowledge that I am right--both types of system behavior are expected to occur.  However, they now claim that their numerical calculations actually include both possibilities, and that they actually did all along!  The letter says that they just explained things really badly.
They have made more changes to the manuscript.  It now states that both behaviors can occur and that both are fully included.  I am not in a position to check whether their results are consistent with this.  The paper does a lot of heavy numerical calculation, which would take a long time to replicate.  Since the bottom line result is not going to depend much on the intermediate details, it is plausible that their graphs could have been showing the fully correct answers all along.
However, the format of the paper suggests otherwise.  Add to that the fact that the second draft of the paper actually argued obliquely against the type of behavior that they now admit is natural.  I feel like there are two likely possibilities.  The first is that the authors are being dishonest.  They missed the second possibility, and now that I have pointed out that it definitely occurs, they have reasoned that they can fib about the matter and it will be difficult to refute their claim.  I am rather unhappy about this possibility; I hope it's not the case.  The second possibility is that the communication between the two authors of the paper was extremely poor.  The person who did the actual writing would have to have been quite ignorant of what his coauthor was actually calculating.  This is a less troubling but still far from ideal situation.
So, once again, I find myself unsure what to do.  Should I share my misgivings with the editor?  Or should I take the current draft's claims as is--chalk the whole thing up to incompetence rather than dishonesty?  I have, after all, no concrete evidence of fraud.

You did not consider that they may have reused some code written by someone who is not an author.  Maybe you should ask the authors to provide the program for review.

You haven't been able to check all their calculations, you can't be sure their explanation is wrong, you're drawing conclusions based on the format of the paper, and your first thought is that the authors may be behaving dishonestly.  I think you're jumping to conclusions. I suggest you step back a little bit and focus more on the substance of the paper and less on the authors' motivations.  Never ascribe to malice that which can adequately be explained by incompetence/sloppiness/miscommunication/inadvertent error/oversight.  They explicitly said they explained things badly -- it happens!

Can I ask whether you think there is dishonesty in the new draft of the paper or just via the discrepancy between the way the authors are presenting their knowledge from the first draft to the current draft?  To me this is an important distinction: "I knew it all along" may be an instance of dishonesty by an academic but not an instance of academic dishonesty.  Almost every person I've ever met tries to frame themselves in the best possible light, to the extent of taking liberties with objective reality.  If this bending affects the academic work itself: problem.  If it doesn't: problem?

@D.W. If you haven't been able to check the results (for whatever reason), should you ever recommend accepting the paper?

"However, they now claim that their numerical calculations actually include both possibilities, and that they actually did all along!" ...what exactly do you see wrong with this? Initially they interpreted the data one way, and now a different way. And they didn't even say "we knew it all along"; they said "our results supported this all along", which seems like exactly what you (as a reviewer) would want to see if their data were obtained properly. What are you really seeking in their paper at this point? Are you upset they haven't given you credit or something?

Why not just demand a copy of the code that was used to perform the calculations. Then you can see things for yourself.

Maybe the numerical calculations did always include the other behaviour, but they hadn't realised this until you convinced them of its importance. Having realised this, they have updated the paper accordingly. In which case everything is fine and correct and the peer review process is working as intended.

I have found myself in similar situations in the past. However, in such cases, the fraudulent behavior was much more obvious. This is a bit of a mixed case.
So long as the results are plausible, the authors do have some liberty to do what you say they have. However, given that they should have also known that the same referees would review the paper again, the way they have addressed the changes does seem to be in poor taste.
I would recommend mentioning what you find problematic in your comments— confidentially to the editor if necessary, but preferably in the "open" comments the authors get to see. This will make it clear exactly why you believe the paper should (or should not) be accepted. You could state something like the following:

Scientifically the results are acceptable, but I am highly uncomfortable with and concerned by the way in which the authors have incorporated the changes. The editor should take these issues into consideration before rendering a final decision.

That puts the decision in the editor's hands (where it rightfully belongs), while you get to make whatever case you feel is appropriate.

I've no clue why is the other answer who got all the upvotes, this one seems reasonable and to the point, while the other one basically says "you suspect something, don't bother, go on, everything's nice" which is a wtf for me.

@Lohoris I addressed your comment in my answer. Maybe this makes it clearer what I meant.

It is hard to tell from just your description alone (and without knowing the actual paper and revisions), but honestly this does not really sound like unethical behavior to me. Basically, what seemed to have happened was the following:

Authors hand the paper in. You review and claim that one of two important aspects is missing.
The authors disagree and, in an attempt to calm you over, write a short token paragraph about the second aspect (that they, in all honesty, don't believe to be very important).
You insist that the second aspect is super-important and needs to be covered more explicitly.
The authors give up and describe what the may think to be a minor aspect in much more detail than what they originally planned to do. Note that it is well possible that the authors were fully aware of this and had the data to do this all the time, but just did not think about it as something important. That they now agree that this is super-important sounds more like an attempt to win you over than anything else.

I have seen these kinds of exchanges play out in many, many journal submissions. Whether this actually improves the paper or not depends primarily on whether the second aspect was actually important or not. You say it is, but I have definitely seen reviewers get hung up on absolute sidenotes as well (which I am sure they honestly thought were essential), ultimately to the detriment of the manuscript.
More generally, it sounds a bit like you were a bit the "damned if you do, damned if you don't" kind of reviewer in this process. What kind of change did you expect after the second review round? You wanted this additional behavior covered, and they did - and now that they did, you wonder why they did not do so from the beginning and presume that something unethical is happening. If they wouldn't have addressed this point, you would have been unhappy because the authors did not fix your major comments.
What I generally try to teach my students is that when you review for journals, you need to recommend fixes that can actually be done. That is, there needs to be a reasonable and doable change that would actually make you happy to accept the manuscript. If you think that a paper can in no reasonable way be fixed, recommend rejection (and don't accept another review if the editor decides for revision). Never recommend revision if you won't like any outcome.
Edit based on Lohoris' comment to aismail's answer:

I've no clue why is the other answer who got all the upvotes, this one seems reasonable and to the point, while the other one basically says "you suspect something, don't bother, go on, everything's nice" which is a wtf for me.

Accusing authors of fabricating the data is a serious suspicion and should not be raised lightly if there are other plausible explanations. It's not that you "shouldn't bother", it's that it simply does not sound like the data has been fabricated to me (or, at least, it does not sound significantly more likely than in any journal submission where the code etc. is not public, which is a systemic problem). There are any number of (good or at least less bad) reasons why the authors may now have data that they did not report on in the first version, including:

The authors generated this data in the many months between revisions
The authors had the data all along but did not consider it very important
The authors wanted to save up this data for another paper (either to salami-slice, or because they truly thought that those should logically be two separate papers)

It is widely accepted that even major changes to the data and analysis can happen in between major revisions, and indeed it is common-place at least in my community to add more data to the paper based on reviewer request. This is basically the reason why we do peer review in the first place - if we always remain suspicious when the authors do more than cosmetic changes, why do we recommend revisions in the first place?
I understand that the authors were talking about a "miscommunication" in the response letter, but frankly, after two revisions (especially with a very critical reviewer in the loop), most authors tend to write in the response letter whatever they think gives them the best chance to calm the reviewer over. I would not give too much weight to whatever people write in this non-public document.

I don't disagree with your last paragraph, but the problem in this case is that the OP didn't know whether the fix could be done: the paper failed to consider a certain possibility, and the OP didn't know whether this was because that possibility could easily be dispensed with, vs. whether this was a fatal flaw that potentially invalidated the paper's whole conclusion. What do you recommend in that sort of situation?

I'm afraid that you have not entirely understood the situation.  I'm sure the misunderstanding is my fault, since I have been rather elliptical in my statements, not wanting to reveal too much about the scientific content of the paper; moreover, the history is spread over two separate questions.
Let me try to clarify:  The first draft only considered one possible behavior pattern for the system.  Anybody knowledgeable about the field would know that if the second behavior patter occurred, it would be important.  My conclusion is that the authors have entirely overlooked the second possibility

... After I pointed this out in my first report, they did not address the point at all in their response letter.  However, I discovered they had added a section trying to show the second type of behavior was impossible.  Yet their calculation contained an error, and it in fact showed the second behavior was extremely likely!  The third draft acknowledged this, but it claimed that the numerical calculations had always included both possibilities.  Either they are being dishonest, or the person doing the calculations is not communicating with the person writing the explanations.


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Too burnt out to continue MSc
I finally realized that there is a term for the situation I am in,"burned out". What can I do to recover from a short term burnout?
I am at the the last four months of my MSc program and I haven't started my thesis yet. The reason was complicated, the MSc program was quite different from the one I had in mind. So, from the start I felt I was wasting my time by continuing at the program. The only reason I didn't quit is because the scholarship is sponsored by the company I used to work. I am still their employee and I am expected to join them once I finished.
If I quit, I wouldn't get promotion not to mention the "he wasn't able to graduate" rumor. Besides, I am supposed to serve three years as a pay back for the scholarship they gave me.
Despite my adviser's genuine effort to help, I am not able to pull myself and do something. I continually failed to see my adviser despite having an appointment with him.  The only thing that crossed my mind for months as a solution is SUICIDE. I am deeply stressed and have presumably stress initiated headache that lasts for weeks.
What would you do if you were in my position?

You already found that question. Did any of the answers to that question help you? Try some of the suggestions people made. For example, I personally like Dave Clarke's idea - take a few weeks vacation. If you cannot afford to three weeks, at least take a few days off, go somewhere else, refresh your mind. It's not the end of the world, trust me.

I'm sorry that you're feeling this way: it's a difficult situation to be in. Does your university have a counselling center ? If so, it might be good to drop by and have a talk with them as well.

Trying to work as a volunteer for an NGO would be a great step in order to find your motivation to study again.

Very anecdotally, I had a similar thing when finishing my undergraduate studies, I even wrote an essay on that Zapaleńcy i wypaleńcy (Flames of passion… and of burnout, or: about loss of motivation during studies; in Polish, yeah, I should translate it one day, but there are many place-specific things), as I observed I am not the only one. However, what saved my thesis (and perhaps much more) was one prof. mailing me about a side project (on a very different topic), which I enjoyed a lot (and then, I got energy to finish my thesis).

Talk to people. Here, elsewhere on the internet, but most importantly in real life, whether it's with people from your program (if you feel like opening to them) or at a local discussion group or whatever. Don't isolate yourself.
Recognize what you are experiencing: describe its symptoms, put a name (or names) on it, identify it. Recognize that it is quite common, and that there are solutions.
Make some time, even if it's not much, to do something else (vacation, volunteer work, some time with family, …) to put things in perspective. This should help you evaluate your overall goals, and decide what you are willing to do to achieve them.
Talk to key people involved (advisor, your boss at the company), and let them know in a professional way that you have hit a bump in the road.

From you post, it sounds to me that you've made a great deal of progress on that path already! You'll get through that hard time, believe us.

I'll maybe add something more personal, regarding the “if I quit, I wouldn't get promotion” part: advancement in your work is not the ultimate goal, it is only worth pursuing if it makes you (and the people around you) happier, directly or by achieving other goals. If it makes you miserable, you have to realize (and accept) that failing is an option.

That last sentence is quite enlightening. Thanks.

To me it sounds as if you might have depression. I'm no expert though, I just have a few friends with depression and so have read a little about it (most effectively by playing a game called Depression Quest, actually!). It might be worth seeing a doctor about it if you think that could at all be a possibility.
I'm hesitant to give any further advice because of this.  A lot of advice that ordinarily might be helpful just for burn-out might turn out to be unhelpful if you are actually depressed.  (Although all the advice given by F'x looks fine as far as I can tell, except potentially you would have to be quite careful with what you chose to do for 3, as some options might make things worse.)

+1 if you are really depressed, get professional help ASAP.

You seem to be having suicidal ideation. This is a significant indicator of depression. Go get professional help ASAP.

@Paul:  You mean "if you really are depressed" rather than "if you are very depressed", right?

@TaraB Border between deep burnout and a depression may be very thin to non-existent. Or perhaps, there may be very tightly related processes, supporting each other.

@Piotr:  Yes, I read a little more about burnout since I wrote this, and you are right.

While others have commented on the number one concern you should have right now (get professional help, be it in the counseling centre at school, a clinical psychologist, or whomever), I will make one comment with respect to the academics:
You probably have more time to finish than the "four months" you mention. Even if you miss that deadline, you will probably be allowed to turn in your thesis later (especially because this is a health related delay).
Talk to your advisor. He will probably appreciate you coming clean with him. Many students finish later. It's not a race.

I continually failed to see my adviser despite having an appointment
  with him. The only thing that crossed my mind for months as a solution
  is SUICIDE. I am deeply stressed and have presumably stress initiated
  headache that lasts for weeks.

Based on this, I would strongly recommend seeing your university's psychologist or counselor immediately.
Do not rely on advise over the internet. Probably, no one here is qualified to help you. Even if there is a qualified mental health professional here, he/she will not be able to assist you without talking to you in person.