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Any risks down the line choosing maths vs. stats PhD programme?
It seems funding for a PhD (at least in the UK) is a lot easier to get for a PhD in statistics rather then maths. Provided one can find a supervisor who has the background and research interestes that would allow one to keep extending ones pure mathematical toolbox are there any major risks in going for a PhD in stats ?
It seems to me that at any given stage there is a lot of flexibility with a degree in mathematics. (For instance people seem very open minded to let a maths MSc do a stats PhD). Is it justified to be worried about losing this flexibility if one opts for the PhD in stats rather then maths?
In particular I am worried about whether it is possible to transition back to the maths departement for a postdoctoral position or something equivalent. So essentially I want to know whether one will get branded to an extend that would make it difficult to go on and work in pure mathematics after the PhD.

Looks much better. There are a few mathematicians on this site who can hopefully provide a good answer now.

Provided one can find a supervisor who has the background and research interestes that would allow one to keep extending ones pure mathematical toolbox are there any major risks in going for a PhD in stats?

There's a small risk, but it can be managed.  The first issue is that you need to make the mathematical content of your work very clear, for example by publishing in journals that could be considered both math journals and statistics journals (e.g., IMS journals).  However, if you're interested in math departments I assume you'd be doing that anyway.
The slightly more subtle issue is how mathematicians view statisticians.  There's sometimes a mild prejudice that people in mathematical statistics are overly specialized and outside of the mainstream of mathematics.  For example, it's possible to get a Ph.D. in mathematical statistics while having considerably less breadth of mathematical background than would be considered acceptable for a math grad student.  (Of course, the flip side is that you are expected to know other things instead.)  There's sometimes a fear that a statistician would be unlikely to talk much with other math department members, or might be unwilling or unable to teach anything but statistics.
Plenty of statisticians have found jobs in math departments, so I don't want to be discouraging.  However, I'd recommend focusing on mathematical breadth.  For example, if you work with people in combinatorics or algebra, then it will be clear to everyone why a math department is a natural fit.  If you talk only to statisticians, it will be less clear.  It can still work out even then, but generally when the department either has a thriving statistics group or has decided they really need a statistician (and either way this cuts down on the flexibility of your job search).

I cannot answer about stats/maths directly, but in general the department you get your PhD in matters less than who it is with. This is especially true in places like the UK were there is no course work component. In my current department, psychology, about 50% of the faculty, including myself, did not get our PhD from Psychology departments. That said, if you are only willing to teach in a Maths department, then you should probably go to a Maths department. If you are willing to teach in either Maths or Stats, then it doesn't matter too much.


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Writing my speaker's bio when I have very little relevant experience
I started a new job a month ago, the first one after my PhD employment. Recently, a conference organizer invited an established researcher from our group to give a talk at their conference. The researcher was not available at that date, but suggested that I hold the talk, which the organizer accepted.
Now I am required to submit a "speaker biography", which I assume will be printed in the conference programme. There are parallel sessions, so I expect the speakers' biographies will have an impact on people deciding which track to attend.
The problem is that I have very done little in my career to interest the conference goers, whose background is in a completely different field than mine. None of my previously published research is of much relevance for this conference. The content I will present interests them, but it is the work my colleagues did before I joined the group.
So I see a few options:

Leave the biography at 50 words stating name, institution and current project. It will probably appear very short in comparison to the other speakers' (upper limit is 250 words).
Add previous work, even though it is neither relevant nor impressive (the PhD is not yet finished).
Write a bit more about the current project, which is relevant for them, but not exactly biographical. And also awkward to phrase without giving the impression that I am responsible for the work that has been done so far.

Due to a tight printing deadline and it being vacation season, I cannot get advice from the conference organizers about what they would prefer. I also don't have access to earlier years' speakers biographies, or to any other material from earlier years of this conference (abstracts, programmes, proceedings, etc).
What would be a good strategy for writing the biography in my case?

Have you looked at speaker bios from previous conferences?

@Kimball I don't have access to speaker bios from this conference. Random bios from other conferences list the speakers by boasting about their impressive job titles ("director of research center for X"), current responsibilities ("manages the EU initiative for Y"), or awards. I have none of these. They also mention current research interests. I have not yet found a research interest in my new field, and I am currently filling a mundane role which gets an important job done but is unknown to scientists and is outside of the academic pecking order.

Don't overthink it. It's true that people will be attracted towards some talks because they are given by a "big name", but it's also true that people will also base their choices on the content, regardless of the speaker. The majority of attendees, if they see a talk on a topic that is of interest to them, will not turn it down just because the speaker has an unimpressive bio. In fact, I suspect many people don't read the bios at all, certainly not all 250 words!
I would recommend a cross between 2 and 3. Give a brief mention of your previous work, because it's a part of your background, and if nothing else, people like to know about the different career paths that others have taken. Then perhaps describe why you moved to your current role/area (avoiding phrases like "filling a mundane role"...), with an emphasis on what you find interesting about it and any ways that it links with your previous experience. That should allow you to talk a bit about the project while still keeping it relevant to you personally. You can keep it fairly brief though - don't feel like you have to use up all 250 words just for the sake of it.

I understand your dilemma but it will not be the last one (sorry to be blunt :-)).
You have been proposed (not for marriage) by an established researcher. He did not give your name out of nowhere, so there must be a reason why he trusts you. The very first thing you should do is to trust yourself.
The length of your resume is not important. You are a young researcher and everyone will be OK with that. Now, did that researcher wants you to be just yourself or to represent your whole lab? I suspect that it will be the second choice so you would be the spokesperson for your lab and, then, you should speak about your lab and not about yourself. Of course that means you should know everything your lab did.
I know you have been in your lab for only 1 month. It is uncomfortable to say the least but be sure that your fellow researchers, i.e. the attendees, will not try to take you down.

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What exactly goes into MS by Research?
I recently gave a entrance exam for a graduate program, MS (by course) in computer science. Since I did well in exam, now I am being offered for MS by research. In first semester I can attend 3-4 subjects, at the end of my semester I have to chose my research area and a adviser. I do not have prior research experience nor have published any papers, so I believe it is a wonderful opportunity for me to get into research.
My question is, what exactly constitutes an MS by Research? What are my responsibilities? What should be my minimum contribution for thesis to graduate?

related http://academia.stackexchange.com/questions/8479/what-is-the-minimum-contribution-expected-from-msc-thesis

@seteropere: Good catch. Indeed, I'd be inclined to close this question as already answered (i.e. duplicate).

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How to look for a master's program?
I am looking to join a masters program for next year. Unfortunately, I've found that comparing masters programs is difficult since the information is not displayed the same way in every university page. My question is: what is the best method to search masters, gather their information and analyze so as to make the best choice?
Other questions about searching masters is the following one, which is directly relates to the previous one: does exist any webpage, document or reference sorting masters and giving unified information about them?

To those who cast close votes that I (unintentionally) cleared: after reading the question, I cast the final close vote thinking that this question is too broad to be answerable. Then I read the top-voted answer, which perfectly answered the question. That is why I re-opened... the question seems too broad, but apparently it isn't.

You can also find search tools specific to regions, such as MastersPortal.eu. If you just need the same sort of general information that you will find on a program's Web site, all in one place, this will be useful.
If you know of a specific major that you want to study, many professional associations or organizations will put a long list of programs on their Web sites, see for example this list of linguistics programs at the LINGUIST list or this database of schools from the TESOL International Association. Sometimes these lists include detailed overviews of the programs, courses offered, and estimated costs, but usually not enough information to know about the quality of instruction.
For thorough student reviews and comparisons of schools, see StudentsReview. This Web site has various tools for searching for and comparing the various details of schools, based on your personal requirements, such as the University Comparison and the UltraSearch.

Actually, I've gathered quite a bit of information on the website phds.org. It is specifically suited for researching Ph.D.-granting institutions, but there is also an option for ranking Masters-granting institutions.

When searching for programs I found that I overlooked a lot of good programs when looking at "database" type sites. They are useful for getting basic data and narrowing the programs you look at, but also consider getting advice from people in (or near) your field and (if applicable) looking at publications in your interest area to see where the research is coming from. I also found organizing the information that was the most relevant to me in a spreadsheet useful, even if it is time intensive.


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How to argue against questionable research practices such as P-hacking and Harking?
I have come into conflict with co-authors when being asked to do things that I consider to be questionable.

Once I was told to try every possible specification of a dependent variable (count, proportion, binary indicator, you name it) in a regression until I find a significant relationship. That is it, no justification for choosing one specification over another besides finding significance. The famous fishing expedition for starfish (also known as P-Hacking).
In another occasion I was asked to re-write a theory section of a paper to reflect an incidental finding from our analysis, so that it shows up as if we were asking a question about the incidental finding and had come up with the supported hypothesis a priori. The famous hypothesising after results are known (Harking).

In both cases I refused to comply and explained my reasoning, what led to conflicts with the other party. I tried my best to not sound accusatory (not to give the impression that I doubt the ethics of the other party), but it nonetheless led to attrition and a worsening of the working relationship. In the long argument that followed, I was told that 'social science is not done as the natural sciences,' and that I was 'too inflexible,' 'too positivist,' and that everybody does these things that I was being asked to do. The argument culminated with me being asked to 'stop obstructing the progress of the paper,' what made me feel very frustrated.
Since then I have seen several cases of what I suspect to be this type of research practice. For example, PhD students coming to me to ask about what they should change in their models so that their results come out significant, and people working at the same computer lab as me asking me for the same type of help.
I do consider these things to be seriously questionable from an ethical point of view, and would like to be able to argue against them effectively. However, the other parties are usually experienced researchers or students under the supervision of an experienced researcher. As a young researcher, I feel that I'm at a disadvantage when arguing against. It is often the case that I'm arguing against the instructions of someone who has more experience, publications, and, supposedly, knowledge than I do.
Is this one of those cases where we can't do much but try to be the 'change that we want to bring about,' shud it, and just make sure that we are doing the right things ourselves? Should we speak up more often? If so, any good strategies to be more effective and convincing?
p.s. The tag is social sciences because of my field, but I reckon that this happens in other areas as well, and I welcome input from other fields.

EDIT 1: In example 2), at no moment anyone suggested that we would confirm the new hypothesis in a new set of data. The intention was to pretend that we got it right from the onset, which is why I objected.
EDIT 2: Just to make clear. I am aware of the right way of doing these things (i.e. cross validation, confirmatory analysis in a new dataset, penalising for multiple statistical tests, etc.). This is a question about how to argue that p-hacking and harking are not the way to go.
EDIT 3: I was unaware of the strong connotations of the word misconduct. I have edited and replaced it with 'questionable research practices'

But is it ok to pretend that you had the hypothesis all along? I'm not comfortable with doing that. As you said, we would need to confirm the results with new data or be honest about what we did.

Not acknowledging the post-hoc nature of the hypothesis is close to using deception to overstate the quality of our results.

Just concerning the "fishing expedition": this is nowadays called "Data Mining". It's not a particularly exciting form of doing research, as you get simply a jumble of numbers with more or less significance and have to read in their entrails. However, per se it is legitimate, and people use it to discover hypotheses. I do not very much like that style of research because it doesn't grow out of understanding. But then, botany suffered under the same problem until the Darwinian unification. Not sure I would call it illegitimate, only not very attractive.

The problem is not the lack of theoretical justification, but the willingness to capitalise on type 1 error in order to get publishable 'significant' results. It defeats the whole purpose of using inferential statistics to begin with.

@CaptainEmacs It's only legitimate if you correct for multiple hypotheses (which so few people do in practice), or if you're purely using it as an exploratory method and plan to collect new data to verify any potential findings. Relevant XKCD.

@Roger Fan Completely agreed - when you evaluate the significance of the hypothesis, you have to pay the penalty of having found it from the data. Indeed, see my answer below.

"Misconduct" is generally taken to be a very strong criticism.  People will listen better to you IF YOU DON'T SHOUT -- so let's look for a different word to describe what's bothering you.  How about "shoddy research practices" or "sloppy approach to research"?  Often people are sloppy out of laziness or expedience or ignorance.  But that's different from a willful intent to mislead -- which the word "misconduct" brings to mind for me.

I see. It may have been my ignorance of that connotation that the word has.

Questionable -- I like it!

Unfortunately, you may in a "null field", per Ioannidis. What you're seeing seems endemic to the social sciences, which explains the crisis of replicability noted in those fields.

Unfortunately I have empirical confirmation that it is the case.

See http://andrewgelman.com/2015/12/20/once-i-was-told-to-try-every-possible-specification-of-a-dependent-variable-count-proportion-binary-indicator-you-name-it-in-a-regression-until-i-find-a-significant-relationship-that-is-it-no/

Wow, now that is someone whose opinion I'd like to know. It seems that I'll be reading that paper he is referring to.

Are you kidding me? Of course it's unethical. Of course it's misconduct. Shame on anyone saying otherwise. Bravo, Kenji.

@BenCrowell In any field where the results matter doing this is straightforward misconduct. If you were prescribed a drug by your doctor would you be happy to learn that the paper supporting the use of the drug had been written by someone who used these methods to get "significant" findings for a worthless drug? Researchers doing this literally kills people. Faffing about and pretending they don't doesn't help. It's less serious if the results don't really matter. But if they don't matter why are you doing the work at all?

Have you thought about asking them to justify each action? If they ask why you want to know, tell them you want to learn. Why would someone drive tests to that direction? Collect some evidence about what they are proposing with their justification and bring it to someone with more experience and verify if action+justification makes sense.
When things become "dark" or even "strange", ask a "higher instance" to arbitrate over the situation. Bring everything to that person and ask him to let you stay anonymous as much as possible. Like as if it's his idea or an audit point of view.

@gnometorule It is as if I throw a dart at the wall and then built a target around it.  Of course, I hit a bulls eye every time - without even trying.  If you revise a hypothesis based on empirical data you should collect new data.

@gnometorule: "A key point in proper statistical analysis is to test a hypothesis with evidence (data) that was not used in constructing the hypothesis. This is critical because every data set contains some patterns due entirely to chance. If the hypothesis is not tested on a different data set from the same population, it is impossible to determine if the patterns found are chance patterns." -- https://en.wikipedia.org/wiki/Data_dredging

@gnometorule Cancer research or sociology, if you formulate your model based on looking at one dataset that dataset can no longer be used to support your model in any context other than saying "after looking at this data I came up with this model". If you present it as "this is my model and it's supported by this analysis I did of this data" without mentioning that you constructed the model only after looking at the data that's research misconduct because you're back to constructing the dartboard around wherever the dart landed while implying that the dartboard was there all along.

Please take extended discussion to [chat].

Ask that the methodology is described accurately in the paper, and provide the description you'd like to include. Describe what you did without unnecessarily pre-judging it. If "everybody does this" then that should be perfectly fine to include.
If you get resistance, or worse, someone later drops the description, then you need to talk privately to the dean of your department, provide examples (without naming) and your interpretation of the scientific value of those examples, and ask how the dean wants science to be done at the department.

@BenCrowell: You might try asking on stats.stackexchange to understand why this is unethical.

Kenji,
For the last few years, I have given a continuing education course called Common Mistakes in Using Statistics: Spotting Them and Avoiding Them. I hope that some of the approaches I have taken might be helpful to you in convincing your colleagues that changes are needed.
First, I don't start out saying that things are unethical (although I might get to that eventually). I talk instead about mistakes, misunderstandings, and confusions. I also at some point introduce the idea that "That's the way we've always done things" doesn't make that way correct.
I also use the metaphor of "the game of telephone" that many people have played as a child: people sit in a circle; one person whispers something into the ear of the person next to them; that person whispers what he/she hears to the next person, and so on around the circle. The last person says what they hear out loud, and the first person reveals the original phrase. Usually the two are so different that it's funny. Applying the metaphor to statistics teaching: someone genuinely is trying to understand the complex ideas of frequentist statistics; they finally believe they get it, and pass their perceived (but somewhat flawed) understanding on to others; some of the recipients (with good intentions) make more oversimplifications or misinterpretations and pass them on to more people -- and so on down the line. Eventually a seriously flawed version appears in textbooks and becomes standard practice.
The notes for my continuing ed course are freely available at http://www.ma.utexas.edu/users/mks/CommonMistakes2015/commonmistakeshome2015.html. Feel free to use them in any way -- e.g., having an informal discussion seminar using them (or some of them) as background reading might help communicate the ideas. You will note that the first "Common mistake" discussed is "Expecting too much uncertainty." Indeed that is a fundamental mistake that underlies a lot of what has gone wrong in using statistics. The recommendations given there are a good starting point for helping colleagues begin to see the point of all the other mistakes.
The course website also has links to some online demos that are helpful to some in understanding problems that are often glossed over.
I've also done some blogging on the general theme at http://www.ma.utexas.edu/blogs/mks/. Some of the June 2014 entries are especially relevant.
I hope these suggestions and resources are helpful. Feel free to contact me  if you have any questions.

This sort of thing happens in both the social sciences AND physical sciences.  For instance, often a scientist will collect data to test a theory but will also collect lots of extraneous data.  Analyses on these extraneous data often should be considered exploratory and labeled as such (because significant results could be due to the multiple tests)  [As another example, you don't want to know how often chemists repeat an experiment until they get a good yield, then stop and report that yield without mentioning that that was the best in 20 experiments!]
The fastest solution is to agree to do the multiple analyses, but then tell what you did in the methodology section.  If you say that you analyzed it several ways and one way showed significance, readers can decide whether or not to believe the result.  Just tell your co-authors that not mentioning that you did multiple analyses is leaving the research improperly described.
However, you can (occasionally) save the day.  If, for instance, you did 10 different analyses and picked the best one, you'll be ok if the result would hold under a Bonferroni correction (i.e. instead of requiring significance at the 0.05 level, you require significance at the 0.05/#tests level).  So it the final test shows a p-value such as 0.000001, you probably are on safe grounds.
Another approach is to a priori decide that some tests are obvious (confirmatory) and some are just searching around the data (exploratory).  Then you can demonstrate the confirmatory results, while labeling anything interesting among the 'exploratory' results as 'needs further research'.  That is, you can mix well-founded tests with 'data dredging' as long as you acknowledge the difference between the two sets of tests.
But if it isn't possible to rescue the result, I'd go with insisting that they describe what they did, with the comment that if they are embarrassed to describe it, they shouldn't have done it.  :)
You might also add that it is often obvious (at least to statisticians) that a researcher has pulled this trick.  When we see a test in isolation that would not occur to us to be the obvious approach, or a hypothesis that we'd not choose a priori, it looks suspicious.  For instance, I recently read a paper that claimed that a certain group of people tend to commit suicide more often if they were BORN in the Spring.  It was clear that JUST testing the effect of birth in Springtime was not something that would occur to anyone, without testing the effect of birth in other seasons.  So they probably had a spurious result due to multiple comparisons.

Yep, I know about all the ways to deal with it: correct for multiple comparisons, use cross validation, etc. I would not object if we would describe what we did or if we would use one of these techniques. The problem is that it is not what I am asked to do (also not and what I see others doing). Upvoted you for the 'if you are embarrassed to describe it, you shouldn't have done it.' I will try using it next time.

You don't correct for multiple comparisons after you've done all of the analyses. Of course, this is about decisions. If you you a priori were not going to keep the significant effect unless it passed Bonferroni then it's OK but if you were going to keep it regardless then the correction is invalidated. You can do the simulations but it's not hard to imagine why that fails considering the p for null effects is uniformly distributed. .05 if just as likely as .00001.

This is an excellent question. I do think you (and others in similar situations) should speak up, but I realize this is very difficult to do. Two things I'd suggest:

Try to figure out if the people you're dealing with understand that the methods they're proposing (p-hacking, etc.) are dodgy or not -- i.e. whether it's an issue of ethics or ignorance. This is harder than it may seem, since I think many people genuinely don't understand how easy it is to find patterns in noise, and how "researcher degrees of freedom" make spurious patterns easy to generate. Asking people, non-confrontationally, to explain how doing tests on "every possible specification of a dependent variable" and selecting those with "p<0.05" corresponds to <5% of "random" datasets having a feature of interest would make this clearer, and would perhaps give you insight on the question of ethics or ignorance. I'd bet that a good fraction of people aren't deliberately unethical, but their cloudy grasp of quantitative data obscures ethical thinking.
Something I've found helpful in related contexts is to generate simulated data and actually show the principle that you're arguing. For example, generate datasets of featureless noise and show that with enough variables to compare between, one can always find a "significant" relationship. (Obviously, without correcting for multiple comparisons.) It may seem strange, but seeing this in simulated data seems to help.

Good luck!

This is a nice idea. I have tried this myself for quite some years now. (This is how I know it's nice.) Unfortunately, it takes a specific kind of understanding for randomness to really understand what your examples in 2 mean in the context of p-hacking. And this mindset is rare among social and medical scientists. I have been able to instill some in a few people I have been working with for years now. It's a long-term educational strategy.

I think there are quite a few comments on the original post betraying the ignorance you describe....

Your instinctive concern about creating hypotheses out of data and pretending they were there from the outset is on the right track:
In statistics, the so called chi-square test can be used to compare data with models which have been fitted out of the data themselves. However, for this, the chi-square test must be adapted to essentially "penalise" one's extraction of the parameters when testing how significant the match is.
This is not easily generalised to other setups, so in general learning theory and practice, one splits the data into multiple groups.  For example, where one part is used to optimise the parameters, one, at first unseen, part is used to optimise the generalisation, and the last, unseen, part never feeds into the model construction and is used to test how well the first two stages worked. This is called "cross-validation".
Perhaps you can suggest (or simply introduce) to your group such a methodology, by splitting the data randomly into different components; out of one you construct the model, which then is tested with the unseen data. Details of how to do the split would depend on your domain. This way, you have the confidence that the model is predictive. For this to be sound, you need to make sure that it is not using the complete dataset in any form (not even through one smart colleague that remembered that the data are parabolic on the whole). Best is to not ever look at the unseen data until the model is complete.
As for post-hypothesising, I found this often not even to be necessary. You might start with a hypothesis, then discover it is not valid, but then find another, interesting phenomenon instead. This is called "discovery" and the coolest papers result from that. If the top journals of your field do not accept such a style, because they want the standard "hypothesis-experiment-validation" cycle, then the problem lies deeper in your community than with your colleagues.
In short: fitting models out of your data and comparing match is ok if you have a way of penalising that extraction (as in the chi-square). Failing that, you can do "cross-validation" for sound results. Finally, instead of post-hypothesising, my suggestion is to hypothesise, say, invalidate the hypothesis and demonstrate the emergence of a different hypothesis.

Yeah, what you wrote is in general in line with what I was taught. It is not like no one in my field knows of it, it is just that they seem to not really appreciate how problematic it is to just p-hack and use post-hoc hypotheses all the while pretending that we did it by the book.

Then, this is indeed a problem. One cannot change easily the culture of a place, it's better to go somewhere else with a culture of integrity.

Or stay where you ar but collaborate with folks from other institutions.

Describe exactly what you have done in the paper. As long as you are honest, the paper will be judged by the reviewers, editors and readers.
Even people doing p-value hacking will have a hard time removing an honest description from the paper. If they tell you to remove it, ask them why and you will have the upper hand in the resulting discussion.

This is the ethically and scientifically correct answer but may be somewhat naive, the challenge isn't knowing that this is the correct answer, the challenge is to get the other authors to agree to actually publish it with all this info. In an ideal world #overlyhonestmethods https://goo.gl/wC76up would be perfectly acceptable and "professional" to include in a research paper.

Lots of good answers already. However, in academia, it's always better if you can back up your position with a nice published reference. Happily, the question of p-hacking and replicability is being raised and addressed more and more often in different disciplines.
I'll set this up as a CW post to collect pointers to relevant publications we can use in discussions with coauthors that don't see the problem with questionable statistical practices. Everybody, please feel free to edit with your discipline's relevant articles or conference papers.
Psychology

Here is an editorial by the Editor-in-Chief of Psychological Science, which is pretty much the mother of all psychology journals (Open Access. I also recommend papers cited by Lindsay.):
Lindsay, D. Stephen (2015). Replication in Psychological Science.
Psychological Science, 26, 1827-1832. DOI:10.1177/0956797615616374.
Here is a study in Science that shows that we indeed have a "replicability crisis" in psychology - a large collaboration set out to replicate 100 effects reported in well-regarded journals, and only 36% did replicate:
Open Science Collaboration (2015). Estimating the reproducibility of psychological science. Science, 349, 6251. DOI:10.1126/science.aac4716

One option is to make 'constructive' points. If your co-authors are (as many are) used to different degrees of p-hacking, they will probably not be too happy to hear that their results are unpublishable as they stand.
If you were able to offer a solution to publish the results while also avoiding these bad practises, then few would object. The best way will probably to try out doing bayesian analyses. Here, (in some cases) non-significant results will also be interpretable and thus publishable.

I hope you don't mind, but I want to take this chance to give you a different set of advice from what you are asking for - to advise you not to take this approach to tackling this issue at this point in time.
I am making the assumption that you want (i) some level of academic success - enough to support yourself, and (ii) to improve the quality of research, and the social benefits that result. Assuming I am correct, I don't think you should  pursue this argument (at this stage).
I don't think you should pursue this argument (at this stage) if you value your academic career as it will cause you to burn important bridges and close doors. For instance, if your supervisor p-hacks and you expose and destroy him for it, then you will have lost your main support and dramatically reduced your likelihood of being able to secure a career in this area.
As related to this, (at this stage) I don't really think that you are optimally placed to challenge the negative influence of p-hacking. Here are a few reasons why I think this. First, as related to what I said above, if you are in the infancy of a career within a specific social system then you cannot easily impact the behaviour of those who are already established in that career, and who neither know nor respect you. Second, you cannot fully understand why the system operates as it does, nor the levers that need to be pulled to change that operation, until you are more familiar with it. You might be able to make a micro level difference (e.g., you expose some people you work with), but I don't see that as likely to be very effective, as it will cripple you to do so.
To sum up my thoughts with an anecdote: imagine that you grow up in a city where all the teachers are corrupt and incompetent. Do you think it would be best to protest against them when are in the school? Probably not, as you would achieve very little, and the teachers would probably use their power to prevent you from graduating and essentially ruin your life. Alternately, would it be better to tolerate the teachers flaws until you are out of the system (or higher up) and in a position to actually change things? I would think so as in that case you might end up in a position of authority, and have the resources available to do something to change the teaching system.
Of course, all of this is just my opinion and I could see many ways in which you could argue against it :)
28/12/2015: Adding some more content to explain and address comments.
I would like to put more emphasis on my main point; to appeal to pragmatism and wait for a better time to act. Personally, I think that there is a time and a place for activism, that sometimes it is best to keep your mouth shut and wait until you have a better chance to do something rather than to speak up and get shot for nothing. Thus, in any case where activism is an option, the decision whether to engage in it should be contingent on various consideration, such as the severity of the undesired outcome, the risk to the individual in preventing it, their ability to prevent it, and their moral framework (e.g., deontological or utilitarian). As the saying goes, you need to pick your battles; every battle will take its toll and some tolls might not be worth paying for what they get you.
Personally, I feel that if you are going to publish something that might wipe out humanity or end up with someone getting killed then by all means you should make a personal sacrifice to prevent it (if you can do something). On the other hand, if the current 'negative' outcome that you foresee is unethically (by some/most authors current norms) changing the focus of a paper (that 5 people will actually read) to look at one significant relationship (e.g., age and correlation to frequency of cycling) rather than another previously planned relationship (e.g., gender and correlation to frequency of cycling) that turned out to not be significant, and the new outcome is either (i) you will be sacked and the paper published without you, or (ii) nothing will be published and no-one will ever benefit from knowing about the significant relationship that you found, then I am more convinced that engaging in activism is not the way to go (at this stage anyway).
And yes, I accept that my arguments here are flawed simplifications of what is a very complex reality, but I hope you can understand my general point and give some thought to it.

Arguably, there always will be someone above you with sub-par ethical standards, so you may end up to never challenge the system. And if you do, your opponents will simply expose your own works which have the same flaw you will be arguing against. It will be damn hard to sound credible then.

While still a junior researcher, it seems that Kenji is asked to put their name as co-author, or lead author, on a paper that conceals its methodology in such a questionable way, and contains meaningless results. Won't that paper harm their career now? Surely the junior has to speak up in private to ensure that the paper is neither misleading, nor incorrect, nor meaningless, nor even recommend a damaging change to  public policy.

This is not a matter of opinion. It is a matter of ethical practice with real consequence for the researcher career, the advancement of science, and the perception of social scientist in general. At this stage, that field is broken. At this stage, when 60% of published finding cannot be replicated, no one should openly be advising the kind of practices OP mentioned. At this stage, the researcher should be guided by their ethics and not wait until they have enough power to do the right thing. Your comment is terrible advice.

@DaltonHance: unless you are an activist who is ready to devote your life to fight for the right thing; it is harmful and pointless to speak against the status quo: you won't make things better for others and you'll make things worse for yourself. It is better to have an ethical competent scientist who has to compromise sometimes -- work within the system, not against it.

@J.F.Sebastian That's great advice for Michael LaCour and Andrew Wakefield. "Fake it 'til you make it and then fix it." You have presented a false dichotomy. Choose to be either the activist pariah or the ethically-compromised but good-at-heart insider. P-hacking may not be as morally egregious as outright fabrication, but the consequence is the same: erroneous findings presented as scientific knowledge. These things have real consequences, even in social sciences. I need only point you to the vaccination rates in liberal enclaves as a real and potentially deadly consequence of bad science.

@DaltonHance: it is disingenuous, to interpret my comment that way. I say things as they are, not as they should be -- it is the principle difference between Aristotle and modern physics (no real progress can be made unless the world is accepted as it is, not as we like it to be).  The road to hell is paved with good intentions. Consider the actual consequences of your actions, not theoretical one. It is not about opinions, it is about facts -- read the question: the paper hasn't improved, OP working relations are worse.

@J.F.Sebastian Science is not a job, it is a calling. Your advice is fine for somebody working some 9-5 selling people pens or paper. Your advice may even be fine for somebody working in politics, that art of the possible. The problem in your view is depicting the academy as just a job. To accept the status quo is antithetical to scientific progress. Anyone who is espousing your view may hold power temporarily but will be condemned to the dustbin of scientific history. Scientific progress is not made with questionable statistics nor is made by weak-kneed, but perfectly congenial collaborators.

This is how bad systems of all sorts become systematically entrenched. Recommending that researchers use invalid/disingenuous research practices in order to become well-regarded enough to challenge those practices is like telling potential slave-owners to go ahead and get rich using slave labor before speaking out against slavery.

@J.F.Sebastian One final point: your advice exposes OP to guilt-by-association. Consider the case of Donald Green, Michael LaCour's co-author. His reputation is irreparably damaged because of his co-author's misconduct. Even if published, OP may find his co-authored paper picked up in the blogosphere by the likes of Andrew Gelman, or otherwise (and rightfully) questioned. Should OP then join his co-authors in a rebuttal, defending the very practices he/she finds questionable? It is absolute nonsense to suggest that working relations are more important than ethical research practices.

@DaltonHance: that is why I don't like religion: it makes good people do bad things justified by some dogma. What you are saying is dogma that ignores reality (burning bridges is not the way to make p-hacking less common). Unrelated: I object to you calling my comments "your advice" -- my comments contain some statements: they can be right or wrong (based on evidence) -- nothing in it is advice. You should probably post your comments as an answer. See, the last sentence might constitute an advice, though I consider it a mere suggestion to consider the question.

@J.F.Sebastian You're suggesting that opposition to unethical research practices is mere "dogma", and that it's a "bad thing" to encourage people not to go along with unethical research practices? As for objecting to the term "advice," how is "work within the system, not against it" not "advice"?

@KyleStrand: (1) I'm not suggesting it. Don't twist my words. It would help if you point out a specific (literal) fragment from my comments  that you consider to be false and what evidence do you have to support your claim. (2) Agree: "work within ..." sounds like advice -- what I've meant is that it produces better results (less p-hacking in the end) compared to the alternative. To be clear: It is not an advice.

@J.F.Sebastian It sounds to me what you don't like is Science. Scientists are notorious arsonists of bridges. The best dynamite the trestles for good measure. Galileo burned bridges with the Church. Newton and Hooke burned a bridge so ferociously that its light continues to illuminate scientific progress. Science consists of that which can be tested and replicated or else refuted. P-hacking for "significant" results is not science. Maintaining congenial relationships is not part of science.

@J.F.Sebastian I'm not twisting your words. You said "What you [DaltonHance] are saying is dogma that ignores reality", and what he was saying is that OP is describing unethical research practices and that OP should not go along with them. This is a straightforward reading of your comment. As for the "it is not an [sic?] advice" quibble--I'm not really sure why you're opposed to the word "advice." When you say that one course of action is better than another, that is advice. That's not pejorative, either; advice isn't a bad thing.

@J.F.Sebastian DaltonHance's last comment is maybe stronger than I'd have put it, but I agree wholeheartedly with "Maintaining congenial relationships is not part of science." It's not incompatible with science, but in this case it sounds like there's a conflict between congeniality and sound science. A scientist should never sacrifice research-ethics in order to maintain a good relationship with someone who advocates unethical research practices.

To respond strictly on Peter's answer here, granted that the OP is an adult (not a minor dependent in grade school), the OP should take this stage as an opportunity to reflect on whether this field, with its given practices and quality of published results, is one to which the OP really wants to dedicate their life.

Comment on the 28/12 edit: "... nothing will be published and no-one will ever benefit from knowing about the significant relationship that you found... ". They almost surely did NOT really find a significant relationship (subject to a real follow-up test). It's illusory; that's the whole problem with data dredging; and anyone who reads such a P-hacked paper is being misinformed.

Thank you all for your comments, all of which are reasonable and well intentioned. I have not got the time for responses, so here is an attempt to conclude. Do you agree that it would be better to (i) attend a protest where you know a pirori that no good change will occur and that you will be jailed for attending, or (ii) wait for an uncertain, but probable chance to effectively protest later? I assume (ii). Is this analogous to the situation that the OP is in? I think so. However, it is clear that some of you do not agree. None of us can know for sure so I suppose we should leave it at that.


text:
Is it acceptable to ask someone for help revising your paper?
I am an undergraduate and I was wondering if it was appropriate to ask someone (who isn't an author) to help revise the draft of a conference paper that I am writing.
I am new to writing and this would be helpful to get input from as many people as possible.

It depends what you are asking them to do.
You should definitely ask someone (maybe multiple someone's) to read your paper, give you comments about what was confusing/particularly clear/incomplete as well as suggestions on how to improve the paper. (In fact I would recommend this to anyone writing a paper at any stage, not just an undergraduate.) This might be done by your advisor if you have one, and you should thank them in the Acknowledgements section for their help (and personally too, expressing gratitude is always good!)
You should not ask someone a non-co-author to actually edit your paper and make changes to it themselves. Any actual editing should be done by you or your co-authors, otherwise plagiarism becomes a concern.

+1: This is a better expression of what I was getting at in my other comment.

Thanks - I have a few professors that I am good friends with and they said that they would be happy to look it over.

It is not just acceptable -- it is to be recommended!
It is generally a good idea to have people read your work before it is submitted, or in your case, presented at a conference.
Having someone from outside your specific research area read your paper is particularly good. Even if your reader isn't familiar with the fine details, they should still be able to follow your general argument from background, through your method, results, analysis and finally to your conclusions.
Approach your intended reader and very politely ask if they would read your work. Make sure you are clear about what you want from them. If it is a colleague who is familiar with your work, you can perhaps ask and expect questions or comments on the finer points of your work. For someone outside your particular field, you can expect general comments on structure or questions on the broader aspects of your work.
At a previous institution, we were all expected to hand our draft papers around for two internal reviewers to read, neither of whom were to be from the particular research field on which the paper was written. This was a very valuable exercise.

I totally agree that it's great to get other people to read your papers and make comments on them.  If that's what "help revise" means, then sure.  If it means that the other person is actually doing the rewriting...that is less clearly good.


text:
Should a professor recommending me talk about my (bad) grades in his or her letter?
I recently graduated with a bachelor's degree in economics. I'm now applying to research internships and some of these need recommendation letters from faculty.
I did very badly during the first 2 years at college (having scored around 6/10 (=2.4/4) GPA in my courses until the end of my 4th semester) -- however, I learnt from my mistakes and became much more disciplined in my 3rd year, when I scored an average of 8/10 (3.2/4). At the end of 3 years, I had to graduate with a sub-3 CGPA of 6.88/10 (2.75/4).
I did really well on research projects and other quantitative projects at college, which is one reason why I've found professors to recommend me. I understand that my poor GPA will probably put me in a bad position, but what's done is done and I digress. One of my possible references asked me to draft a letter for him and that he will make changes as necessary before sending it in.
My question is: should I ask my references to talk about my poor grades (something along the lines of "While X does not have impressive scores in his undergraduate degree, it can be clearly seen from his transcripts that he picked up pace and discipline during his final year. I believe that X has taken home the importance of hardwork after a few hiccups in his academic life".)
Even if the above phrasing doesn't seem good, in general, is it a good idea to talk about your bad grades in a recommendation letter? Or is it simply better to let this go and concentrate on my strengths?

The idea of the professor asking you to draft the letter is rather controversial, see https://academia.stackexchange.com/questions/29041/what-does-it-mean-if-a-professor-asks-you-to-draft-his-reference-letter-for-you.

Yes, I understand. That said, it's pretty commonplace where I live (India) and in any case the professor is the one who'll be sending it in at the final stage after making the changes he feels necessary.

The bad grades are there on the transcript - something to address them (since they will stand out from other applicants) seems prudent.

It really depends on how the professor knows you.
If the professor only knew you "after the change", the letter might say "I am aware of student's relatively low grades during the first few years, but by the time of my class, they were a top-performing student", that kind of thing.
If the professor has known you for years, it might be appropriate to comment on having observed this change, and expressing confidence that you can handle the role in question despite the low cumulative QPA.

Is it a good idea to talk about your bad grades in a recommendation letter?

If not asked about it, why shed the light on it?
Your professor can talk about your points of strength and your efforts through the years. No real need to talk about everything in details.
If you really feel that the grades by them self are subject to questioning, it will be a good idea to mention them in that way but, in my opinion, everybody knows that the grades alone aren't really a factor and in fact, your grades getting better time after time is the biggest factor.
I would suggest, if you really need to talk about it, to say something among these lines:

X has shown big improvement through his undergraduate studies which proves, with no doubt, that he is capable of handling bigger projects.

I am not a native English speaker so this might need some improvement.
Bottom line is to focus on the good side without really mentioning the bad/down side that got you there.

"Why shed the light on it" --> because the reciever of the letter will see the bad grades and thinks the opinion of the professor is not well justified or even worse it's a standard letter which is used for every student.

The receiver will also see the improvement. This is why focusing on the important side is better in my opinion

The suggestion to focus on the positive is very good; but the initial part of this answer, which seems tantamount to advising against mentioning it at all, is imprudent in my view. As OBu says, the recipient will see the poorer grades, and if they are not mentioned at all will likely become suspicious of the letter’s relevance.

Maybe the way I said it isn't what I meant 100% (as I said, my English isn't perfect). All what I thought about is that : "the recipient will see the poorer grades" -> "talking about them might seem like a defensive stance without a sure attack from the recipient" -> "don't talk about the grades themselfs" -> "however, mention the improvements that OP did (according to the grades)". All of the idea is because the recipient will also see the good grades and will get what the improvement means.. hopefully this made myself more clear

It depends on whether or not they can speak to the "story".
Your letter of recommendation is an opportunity to turn your CV into a narrative, and if part of that narrative is your grades suffered early on for one reason or another, and that's been resolved, it's potentially worth talking about. But not if it's just "Joe used to be bad at this, and he's gotten better." They should be able to talk about your growth, how that came about, your development in new directions, etc.
TL;DR: Yes, but only if they can do it well.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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Are all papers published by ACM indexed in DBLP?
Will a paper accepted by a new conference, whose proceedings will be published by ACM and indexed in Ei Compendex and Scopus, also be indexed in DBLP?
In particular, will the proceedings of ICCBDC be indexed in DBLP?

"and got the answer alone" - if you got the answer, why don't you post it here? You can answer your own questions.

In general, a reliable way to find out in the case of DBLP would probably not be by asking the conference organizers, but the people who curate DBLP.

In the past, I've sent the curator of DBLP an ACM journal which they failed to index.   They promptly changed that.

I think this question can be answered via DBLP's FAQ, specifically: What are the criteria for dblp to index a journal or conference? In a nutshell,

DBLP has a venue application and review process that seems to be applied to each newly included venue,
During this process, a set of minimum standards for newly included venues is considered. One of these standards seems to be inherently fulfilled by conferences published in the ACM digital library: ''All full-texts of contributions should be available online (either openly or as a paid service) in an established digital library.''
There are no guarantees for (timely) inclusion: ''Please note that beside the eligibility of a publication venue, the workload and the resources at dblp are always a limiting factor for the inclusion of new venues''.


text:
Are the Survey rankings for the National Research Council really more objective than the Regression rankings?
This blog post argues so, but I have my doubts since the author works at a graduate program whose S-rankings are much better than its R-rankings. I have a feeling that R-rankings do capture some things that S-rankings don't capture. Professors who are far ahead of their time, for example, might be recognized as such, but I would expect that their papers probably won't get very high citation counts for some time.

Do you mind perhaps elaborating on what S and R rankings are here in the question. Also I assume by NRC you mean the National Research Counsel? Although I do it frequently as well, best not to leave undefined acronyms when possible.

No, both rankings are basically nonsense.  Even if you agree with the NRC's choice of a single "quality" model across all intellectual disciplines, the rankings are based on horrendously incomplete and incorrect data.  This is especially true in computer science.
Also, the claim in the blog post is an obvious joke.  The S-rankings are "better" because  writer's home department's S-ranking was better than its R-ranking.

Any ranking that tries to distill a large bunch of heterogeneous statistics into a single number should be treated with a healthy dose of skepticism. There is no inherent reason why a particular set of statistics should be weighted with one set of weights instead of another—and the two different weights could lead to widely divergent results.
That said, if you see a large number of rankings, and they all tend to have fairly similar results, there can be some predictive power in the collective set of rankings. And I would say it's a "loose" scheme at best: the difference between #1 and #2 on such lists is probably not indicative of much; the difference between #10 and #30 or #50 is much more meaningful.


text:
Suggestions for high school student who is visiting a professor for HS research?
So this is what she sent me:

i emailed a caltech neurobio prof, dr. X, and he emailed me
  back and wants to meet me, im visiting him on june 16...i went through
  this sort of thing with ucla and stuff but this time i'm sooo nervous
  because he's a caltech prof and caltech is caltech...do you have any
  tips for me?

So my general suggestion to her is this: show that you can do something that can reduce the time load on his research (which is pretty much what all PhD/UG/HS research is about) - you want to do things that the professor wants to do but doesn't want to spend the time on, although some professors might be willing to "waste" a little time mentoring high school students and undergrads.

he already asked me what lab and general knowledge i know i told him i
  know gel electrophoresis, pcr, restriction endonuclease digestions,
  immunohistochemistry, and some basic cell culture techniques and that
  i learned some basic neuroscience neuroanatomy and neurohistology and
  how to read fmri images through the us nat'l brain bee competition

I also suggested this: when making a list of your skills, show - don't just tell (although this is really hard for many high school students since they haven't had the chance to show yet). Professors are used to people who claim to know more than they really know.
====
What are some other general suggestions that can be helpful to high school students who are trying to do research with specific professors? Generalizable cases are fine - as I do get quite a few emails from high school students who want to seek my advice on how to get research with a professor.

As a HS student visiting a lab, a willingness to do some of the "grunt" work in exchange for a chance to do "real" research is helpful. Being able, and willing, to do literature searches on a database (e.g., pubmed), to download easy to get papers, and go to the library to get hard to find papers papers is really useful for a lab. Most researchers would happily trade their time teaching about the methods they use and love, in exchange for not having to make a run to the library. Being able to do data entry and carry out statistical tests, with guidance, in a program like SPSS, is really valuable. Being able to make charts/figures in programs like Excel, Photoshop and Illustrator is a real plus.


text:
Which Internship should I take?
I'm a Chemical Engineering major in undergrad, and I currently have two internship offers this summer.
The first one is a 10 week summer research internship at a top-10 program in petroleum and chemical engineering. The internship concludes with a poster presentation of the work you've done. My work here could potentially turn into a publication. It's worth noting that I've technically already accepted this position. I didn't get the offer for the next one until after I had accepted this one.
The second offer is a full summer semester research internship at the uni I currently attend. I'd be working on the research I'm doing now all summer; as a result, I'd almost certainly be published by the end of the summer, possibly twice since my PI has a couple of papers that just need one more topic covered before they get fully published. It's worth noting that this internship would get me almost twice as much money by the end of it.
My question is: which should I take? I have a very good shot at the Goldwater next year, so I need to know if it would be better to be published, or to have a letter of rec/research experience at a top university.
I'm also meeting with my PI next week to see if I could possibly postpone the one at my school to next summer, so I'd have a chance at doing both.
Any advice would be greatly appreciated!

Welcome to Academia.SE. I just wanted to point out I've edited your question to remove the personally identifying information (which schools you have offers to, and are attending).

Welcome to AC.sx. As your question stands of know, it is not really clear how it relates to academia. Which internship to take really will depend on your future goals. The problem I see is that including your future goals then would make this question very specific to you (see localized in the <http://academia.stackexchange.com/faq faq>. Please consider editing your question to make it both on-topic and broadly of interest.

This is a situation not many people find themselves in—having to choose between several very good options.
The basic issues to ask yourself are the following:

Do you have enough high-quality letters of recommendation for your future needs? If you're doing fellowships, you will definitely need three, and may need four. Again, these should be people who can comment on your abilities beyond simply just reciting grades in lectures. These must be people you have had an ongoing working relationship with. Ideally this is a full-time research mentor, but realistically also can include people such as part-time research mentors (such as REU-like work during the semester), instructors for whom you've worked as a teaching assistant, industrial employers (for an internship), and possibly your academic advisor (if it's someone who's advised you for multiple years).
Regarding the letter writers: are they all at the same institution? If so, then it might behoove you to find one at another institution. This will be helpful to you, as it shows you're not just a "system" product.
Is the research that you're doing something that you know you want to do, or just think you want to do? You will in general be more likely to have real success if you enjoy the work you want to do.
Will the pay for the "away" program be sufficient to cover your living expenses and needs? If so, then you should neglect the difference in pay between the two programs, and focus on the relative benefits of the two programs for you.

There's no right answer to this question; you just have to choose which option is easier for you to live with.

Thanks, I think I'll go with the top 10 program, because I could definitely use some more letters of recommendation. Publication can come later if it needs to. I think it might be better to build a good foundation (letters and experience) before moving onto something like publication.

Don't misunderstand my points above: publications can be very useful. But a very good letter from a "known" name is probably more valuable than a co-author credit on a publication. (Not to mention—it might open doors for you at the other school, too!)

In the first bullet point, I would change every "should" to must.  Your letters must address more than your grades, and they must be from people you've actually worked with.

@JeffE: I agree with you on the second point, but the first point is out of the applicant's control.

@aiesmail: No it isn't.  The student can ask point-blank "Are you willing to write a strong recommendation letter that describes more than my performance in class?"

@JeffE: For students in some countries, maybe. In some cultures, including herein Germany, you risk ticking off the faculty--or having them write the same boilerplate letter that they write for everybody else.

@aeismail: I'd be shocked if any of my (many!) German colleagues reacted that way.  But then computer science is different.


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Is a "big ego" necessary for a tenure-track faculty position?
I have worked in a few high profile research institutes for my PhD and postdoc, and find myself a little intimidated by the "strong opinions" and arguments that seem to crop up at the principal investigator level.  My personality is somewhat more laid back or agreeable, and I find myself wondering if this would be seen as a "personality flaw" in the somewhat cutthroat funding and scientific world (or at least that part I am exposed to).  I'm sure it helps to have a strong opinion of one's own work, but I sometimes find it hard to do so, as I tend to be more self-critical and more concerned about stating something correctly and figuring out what is "right" than necessarily being self-promoting and overconfident in my own abilities, but I feel like that ends up manifesting as some sort of fundamental flaw in relation to others I see advancing into the faculty ranks.

No.  At least, it's not a necessary condition in all fields and at all institutions.  It is necessary in some fields, at some institutions.

A "big ego" is not a prerequisite; self-confidence and a thick skin are much more important.

@aeismail Hear, hear! I'll also add that academia certainly has a large share of "big egos", but anytime you have a group of people with power to wield, you will likely have a similar situation. Think politicians, lawyers, doctors, CEOs, etc.

@che_kid I like the idea of academics wielding power

This is a very curious discussion. The OP said, "I see people in academia as having big egos", and then all the replies were "No, we don't, these aren't really egos", etc.

I think a "big ego", or at least a healthy dose of self confidence, is necessary for survival as an academic. Our jobs are one of repeated failure. Success rates on grants, publications, experiments, and job applications are often less than 10%. In the face of such frequent failures an abundance of confidence is a require for maintaining ones sanity. Further, many departments verge on dysfunctional and there is generally someone looking to push you down to get ahead (I am not saying everyone is out to get you, but there is generally at least one person in every department looking to get ahead at your expense.) Being confident, outspoken and to an extent self prompting is useful for dealing with these people. The difficult part is not being too self confident and too outspoken and being able to admit when you are wrong.

I wouldn't necessarily say "big ego." I would say a "thick skin" is a prerequisite.

It's important to distinguish inner from outer perspectives here. Someone that is perceived as 'having a big ego' might not feel that way inside, and might be compensating for a bad case of Imposter Syndrome. Or they just might have a big ego through and through :).
As others have pointed out, what you really need is a thick skin, in order to deal with the constant rejection you'll face (jobs, papers, grants, awards, ...). You can acquire a thick skin by having a big ego ("People are too stupid to appreciate my genius") or by being bull-headed ("I don't care what people think: I think this is interesting"), or by other coping mechanisms. This is an internal focus: it doesn't matter what you show on the outside.
Occasionally though, it's helpful to project an aura of confidence and assuredness, most commonly when you're interviewing, or when you're psyching yourself to write a grant or pitch a project. Again, this is an external focus: it doesn't matter how you feel on the inside.
Ultimately, you'll (hopefully) find a harmonious balance between what you project to the outside world, and how you feel on the inside. They don't have to necessarily be the same view though: they rarely are.

I am sure a psychologist could provide an indepth analysis to a large part of this question. I am not a psychologist so I will try to stick to what I think I can answer. First, it is usually the big and loud heads that stick out and are seen, there are probably just as many quieter academics in similar positions that you do not see or hear.
I am sure it is possible to talk yourself into a top position but not without showing excellence in your science, usually reflected in a publication record and funding success (sadly to a lesser extent teaching). But, the academic record on paper is typically what counts and it would only be in an interview situation between two equally talented candidates that things can be swung. But even then, I do not think the ego would necessarily have an advantage. Then there is the question what happens after you are employed and how one develops as a person but that is out of scope for me.
A problem more timid persons experience is to strike the right tone in pushing ones own merits. Taking advice from entrusted clleagues is a good way forward. But, as a whole a "big ego" in the negative sense is not necessary, a good academic record is.

A "big ego" is definitely not necessary.  Of all the scholars I have interacted with, the biggest-shots were often less egotistical than the average person.  Especially at the highest levels of academia, the strongest work often comes from those who are willing and able to listen to and improve on the ideas of others.
However, you do need to make yourself heard.  You do need to be bold.  I do not know a single successful tenure-track person who sits on ideas until they are sure that they're right.  You don't find out an idea is right by mulling it over.  You find out by formulating the problem rigorously, by testing a hypothesis, and by subjecting it to the most scathing peer-review you can find (not necessarily in that order, as you might do some of these steps multiple times).  There's no harm in being self-critical or unsure that you're right.  There is harm when that hinders you from taking the necessary steps to find out if you're right.

You don't need an ego. In fact, ego just makes life more difficult in many ways. All you need is to able to project confidence, and the ability to assert yourself. A simple ability to stand your ground and stand behind your opinions, even if quietly so.
Think about the guy or gal you know who doesn't talk too loudly but everyone listens when he or she speaks. Try to emulate that person, not the egos in the room. People generally dislike people with big egos, but they love people with genuine confidence. People will respect the hell out of you for it.
There are many resources online regarding how to project confidence--I suggest looking there.

Usually, it is the person with the money, or with the administrative influence, who is being listened to in most meetings.

Academia, as many other fields, is the survival of the fittest game. If you see PIs with big egos (or rather if most of the PIs you see have big egos), then it means that this is the personality trait that academia cultivates, albeit unknowingly, implicitly and subversively.
Academia is a race against time -- if you hesitate about your research idea for too long, and wait to perfect it, you will either be scooped, or will run out of your time in your (presumably rather junior) position. Submitting something that is just "good enough not to disgust the three referees" that you yourself know isn't the greatest paper, and getting it published, gets you the confirmation of "Aha, I am a smart enough person to game the system". Then you learn to salami-slice it to boost your # of publications; then you learn to attribute collaborative teamwork to yourself when your chair asks you, "How many papers have you published this year?" -- all these things sound a lot like "I, me, myself", and occasional big carrots (your R01 grant; your tenure) are obviously about you. When you do this for 20 years in a row, your skin gets so thick that it looks very much like a big ego from outside.
Don't worry, you will see big egos in most other fields at the higher levels -- think real estate development and golf, which are closely intertwined, anyway; you would not find folks in these lines of work very approachable.


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Why the taboo against naming discoveries after yourself?
It is often considered a very bad idea to try to name a discovery (a law, phenomenon or an invention) after yourself.
On the other hand, there are many species names which seem to be a bastardized Latin of the discoverer's name. With gene names, I'm not sure, but I've seen many frivolous gene names, such as those inspired by cartoon characters. I wonder if you could get away with naming a gene after yourself if you wanted to.
In other cases, such as names of synthetic strains and plasmids, it is in fact preferable to use the researcher's name. Off the top of my head, I recall GFAJ-1 - the surrounding controversy aside, given all the work she's done, what is so wrong about her wanting to name it after herself?
However, why is this so? Apparently, it can even advance to an extreme where many years after a discovery has become established and associated with Dr. Jones, when he writes a review article on applications of the Jones Effect he will still carefully avoid acknowledging this name.
Isn't it convenient to simply name something after yourself when no clever acronym exists? Isn't doing the work of the discovery enough to earn the right to name a thing? Doesn't the fact that the discovery is important enough to be published automatically imply that it's important enough to be named after oneself?

Why you would think the "Superbest" phenomenon would be a good name for something? Jokes aside, naming things after yourself is best reserved for cranks and not scientists.

@Alexandros Like I said, ever so often one makes a legitimate discovery which is difficult to name in a descriptive fashion. In such cases, especially when comparing a new, tentative discovery to others, it seems like naming it after yourself is the best (pun intended) option.

Does everything need an explicit name?

@AustinHenley My question is about those things for which having an explicit name is very convenient.

My impression is that many such names are given to the discovery after the fact. The first paper to cite the initial discovery might call it the "Jones effect" for the sake of brevity, and so the name sticks.

Sonic Hedgehog is something from biology. Can you tell me what it is? If you're going to name it after yourself, or something, it helps to indicate what it's about. Newton's theory of gravity. Jones Effect of double-frying fries.

@Compass In fact Shh was named that because the mutant fly embryo looks spiny - this doesn't explain exactly what it does (though already suggests a central developmental role!) but then expecting it to is unfair, since its function was not well known when it was named. Anyhow, have you ever had discussions about "Jones model, Smith model and the model I propose"? That is where the utility is supposed to be.

@Moriarty True, and I am wondering: Why not just cut to the chase, and name it after yourself to make discussion easier? Given how often communities choose to go with that convention, one suspects it facilitates discourse. If it facilitates discourse, why not obtain that facilitation sooner, by naming it yourself?

@Superbest my guess is that that is a rather pompous approach to things. It's your peers that should decide if it is an important enough discovery to earn a perpetual name.

Btw, as well as being frowned on it's probably unwise. What if you name something the "Jones effect" and then a couple of years later discover an even more important effect? You call it "Jones's second effect" and look a proper charlie? ;-) Whereas if the name is informal and takes a while to be taken up, you still have a shot that the important one ends up called the "Jones effect".

Related: http://academia.stackexchange.com/q/34547/10643

@CapeCode Not just related, but inspired this one!

@SteveJessop  Why not?  Newton got a first, second, and third law; why can't I have a first, second, and third effect?  ;P

@Doc: It would be cool if Newton had published the first one as "Newton's law of motion", with great fanfare and trumpeting, received his knighthood, and then came back "oh, hang on a minute, we're going to need another one". Then five years later, "look, you're not going to believe this..."

related

Do you have any links to sites where it is mentioned that this practise is considered taboo?

Is there an existing word for this (discouraged) practice?  I suggested autoeponymity at one point.

While appears to be true that GFAJ-1 was controversial and that the discoverer named it after themself, it appears that the controversy was centered around the extraordinary biochemical claims about GFAJ-1 that were then refuted by other research groups rather than the name itself.  And while naming something after oneself tends to be rather poor form, that particular name appears to have been humorous, making it less objectionable.

In general, the name of a thing should indicate an aspect of it which is very important to the person assigning the name.  A person who creates something and names it after themselves implies that they think the most important thing about it is that they created it.  That would in turn suggest to anyone who isn't interested in the person who created it would likely not be interested in the thing thus named.
Thus, naming something after oneself is not necessarily a sign of ego, but rather the opposite.  If Alex Johnson (made-up name) publishes a paper entitled "Alex Johnson's Laws of Quarkions", the title would suggest that the paper was primarily relevant in relation to Alex Johnson's other work and would have little relevance outside that.  If instead the paper had been simply published "Laws of Quarkions", that would have a stronger implication that the author believed laws described therein to be universal, and thus relevant everywhere.
It is only after the importance of something becomes self-evident that the attaching of the creator's name to it really serves to elevate the status of the creator.  Until such time, the attachment of the creator's name will tend to deprecate the importance of the thing thus named.

"the most important thing about it is that he created it" - I think this was exactly the answer I was looking for. I'll accept since I can't argue with this logic.

Traditionally naming after scientists has been considered an honour bestowed upon somebody by their colleagues. This is why there are plenty of such names in latin animal or plant names. one very good example is Strigiphilus garylarsoni, a chewing louse, named after Gary Larson, author of the Far Side.
Naming can also be subject to strict laws. In some countries, it is, for example, not possible to name official places after persons until after they have passed away. This is to prevent people to inflate their own reputation while alive (it is quite easy to see where such behaviour is going overboard). But, this is a digression.
The main point is that etiquette indicates that one does not name things after one-self, one can hope that the work is so appreciated by others that a naming occurs. It is probably also a good thing that everything we discover is not named after a person. I drove my Smith at 120 kiloJones per Dickens.

Wouldn't you you want a guacamole of Hydrogen molecules, though?

Interesting! But Gary Larson was far from a lousy cartoonist ;). More trivia: the lemur species Avahi cleesei is named after John Cleese (which he regards as a higher honor than the knighthood he declined).

Please, kilojones per dickens. The unit name is with a lower case letter. ;-) (Also, careful you don't get a ticket.)

What's the difference between a kilojansson-richerby and kilovolt-ampere (kVA)? Actually, I guess I do prefer more romantic names, rolls off the tongue better...damn you Watt, Joule, and Tesla!

@NickT Note also that he didn't drive his Smith, but he could have driven his Ford.

One reason is that academia tends to frown on self promotion of all sorts (not just naming things after yourself).  This is certainly not a universal rule, and some areas are more tolerant of self promotion than others, but it's a good first approximation.
Another reason is avoiding conflicts of interest.  A meaningful, descriptive name is better than naming something after its discoverer (imagine if black holes were called something like "Smith objects").  Furthermore, several people are often involved in any given discovery, either as coauthors or as authors of related papers, and it can be tricky to decide who really deserves the most credit.  If you let people name things after themselves, they will naturally have a bias to choose that name instead of a more meaningful or appropriate name.  Ruling this out of course doesn't eliminate all bias, but it's a start.

I don't think the terminological clarity argument holds up very well: we may have back holes, but we also have Wolf-Rayet stars and the Chandrasekhar limit.

@jakebeal: We do have plenty of things named after people, but I believe that naming pattern is worse for the community (it's a small but real increase in barriers to understanding and memory).  This is best reserved for cases in which it's difficult to think of a really good descriptive name or when the community wants to honor an outstanding contribution (and is willing to sacrifice clarity to do so).  Either way, it's a conflict of interest for someone to make this decision regarding their own name.

The one word answer is: culture. The culture of academia — and indeed many other areas of life — has a variety of rules placed on self-promotion and naming. These are, in many way, arbitrary, capricious, and often illogical or unreasonable, but that's just how culture is. It serves various functions, some good and some bad.
The general rule is: complimenting someone is an honor, while complimenting yourself is usually frowned upon. The cultural mores are that you should be concerned with other people's opinion of you and work hard to win their high regards, but you should be humble and uncertain of your own personal values.
This is partly due to a myriad of psychological mechanisms, like the fundamental negative bias (people are naturally better at spotting and remembering negative things), attribution bias, illusory superiority (everyone thinks they are above average), tactics to combat free-loaders/loafers/cons, and vested/conflicted interest.
In short: we are highly skeptical of people who are trying to tell us how great they are. Also, naming things is hard, at the same time as having something named after you is considered an extremely high honor — a truly grand compliment.
If I said, "Man, that Brian guy is really an amazingly great person" or if I discovered a previously unknown kind of rock and named them "Brian rocks", there are many people who would immediately have the urge to punch me in the face.
It is further generally recommended that you avoid doing things that make people instinctively want to punch you in the face. And so it goes in academia. You are free to ignore it and name a tower after yourself or try to place your own name on a discovery, but you should just be warned that some people might not react well to this.
Ultimately, it's just a weird social truth: it's always better to have people compliment you than to have to do it yourself. That's not to say you can't toot your own horn (especially when no one else seems to want to), but it's way better to get yourself a shill.
Man, that Superbest guy asks great questions...
Note: Not all cultures are this way. Indeed, in business it is generally accepted that you can name the business or product after yourself all you want, and self-promotion is often actively encouraged with far less limitations. This is, one might imagine, an area where business and academia don't always see eye to eye.

Oddly enough, I've hardly ever wanted to "punch someone in the face" simply because they named discoveries after themselves. It always seemed like a natural thing, and part of the motivation.

@Superbest Me either, which I think is why it seems so weird at times - I mean, I really don't mind, so why should someone else? Unfortunately that's not how things work - some people mind about all kinds of stuff. While I generally love "those who matter don't mind, and those who mind don't matter" I'm not sure how well this applies in academia. I don't know of any horror stories though, so I perhaps wonder just how bad this is - is it just frowned upon by some, or is it a poison pill? That I don't feel qualified to answer, as I haven't witnessed it either way.

My sense of this is that a name on a scientific artifact is seen as a form of immortality.  Newton's Laws will remain Newton's Laws for untold centuries to come, and Isaac Newton will be remembered as an important figure of science for discovering them, just as Carl Friedrich Gauss will be remembered, and Leonhard Euler, and Edsger Dijkstra, and Marie Curie, and so on.  Even when the students who use their names don't know their biography, they honor their memory.
If you name something after yourself, you are saying that your accomplishment is as significant and deserving of going down in history as the ones that I have named above.  Most scientific work, however, doesn't turn out that way.  Even the work that does is mostly identified only by how it stands the test of time.  So if you name something after yourself, you are effectively saying that you are so smart that you can see into the future and tell that history will judge your work as super-important.
What a massively egotistical assumption!
Note: Some things, like synthetic plasmids, are exempt from this principle because they are not so much immortalizations as card catalog indices.  You are not likely, for example, to find "Janet Wang's plasmid," but rather "pJanetWang-73j-v2" meaning something like Janet Wang's 73rd plasmid, type j, version 2.

"If you name something after yourself, you are saying that your accomplishment is as significant and deserving of going down in history as the ones that I have named above." - But when you submit a paper, you are not suggesting the same for your work? Even greater discoveries have been published, that were not named for their discoverers. Besides, the egotism you describe seems to be self-punishing: If the thing I name is not truly worth naming, no one will bother repeating that name anyhow.

@Superbest When you submit a paper, you are not suggesting the same for your work? Of course not: putting your name on a paper is just signing your work.


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How does the American PhD system works for foreign students?
I'm a MSc Theoretical Physics student from Italy, and I'll most likely graduate around the end of next year: for the Italian academic system, this means that I'll be ready to try to get into a PhD program (after 3 years of BSc and 2 of MSc).
I'm considering other countries' universities too, and since I spent a year of High School in the US I was looking for information about it. For what I understood, their post-secondary education system is college (4 years) for a BSc, then either Master or PhD (? years). Is this understanding correct? If so, do they have special rules for foreign students who already have a BSc and a MSc?

Does this answer your question? How does the admissions process work for Ph.D. programs in the US, particularly for weak or borderline students?

@GoodDeeds not fully, as my question specifically regards foreign students, but it cleans some mist off the subject, so thanks for the link.

Apply and find out.

Before you go, be very careful to find out all of the costs and what funding you can get. Be very sure that you can tolerate the debt you may have to take on, if you must.  In this context Google is your friend. Google up grants and scholarships and tuition costs. Many scholarships require you to apply in order to get considered. Ask the secretary of the head of the departments you are considering. And be VERY nice to that secretary.

In most fields, US PhDs can start right after a bachelor's degree. Some incoming US students will have masters degrees, either because they've been uncertain about their career path and or because they pursue a masters to get more experience to make their applications look better (especially if their undergraduate research experience is lacking).
Because US PhDs usually start right after a BS/BA, they are typically longer than the European variety; 5 years is common, though for some fields they can be much longer, up to 10 years.
Coming in with a European masters (or a US masters, for that matter), there is not typically anything that will allow you to complete the US PhD faster than normal. There might be cases where you can skip some coursework, but that will be handled on a case-by-case basis.
Funding can sometimes be more difficult for foreign students unless their own government provides support, since some funding sources are not available to international students (especially fellowships from the US government). Other funding is available to all (most TAs, sometimes with a more strict language requirement, many RAs and PAs; check with programs you want to apply to about their funding availability).
Many programs and universities will have information for potential incoming international graduate students, you should search the websites for individual schools/programs you are interested in. Some of this additional information will cover things like visa requirements (including funding) and language proficiency. An example from my own institution is here: https://grad.wisc.edu/international-students/
As far as PhDs as a job, I'm less familiar with Italy but have seen the German system explained often here: US PhDs are not jobs like German ones are. That said, almost all US PhD students (and especially in STEM) have some sort of funded position that includes a stipend, and typically this comes with tuition remission, so you take home a (modest) income to pay expenses, while not paying tuition. Other students are on fellowships or traineeships that specifically fund a students' education and include a stipend.
For some fields, these are usually Research Assistant positions, where you work on a research project for pay (sometimes this is the same as your PhD thesis, sometimes not - again, depends on field and circumstance), or Teaching Assistant positions, where you teach or help teach a course or two. Programs typically guarantee some sort of funding for X years; if you take longer, you could possibly find yourself without funding (the likelihood of this depends on the field; I don't know about theoretical physics but my educated guess would be that there are enough TA positions in physics that as long as you're willing to teach you'd find a TA spot). There are also cases where someone might do an unfunded/self-funded PhD - unless you (or your family) are independently wealthy I would strongly advise against this (you will find other Q&A here about it).

At the most you might shave off a year or the equivalent of a semester. That's pretty field-dependent though.

@AzorAhai--hehim Agreed, and probably the only chance for that would be in a heavily coursework-dependent field and with directly overlapping prior courses.

Or perhaps continuing in the same department (although obviously not relevant for this OP).

Thanks, this combined with @Anyon 's comment answer my question. You mentioned funding: is the PhD in the US not a paid job? In Italy they pay you (not much, but at least it's a positive income)

@MauroGiliberti Yes and no; I suspect in practice it's pretty similar, though; I've edited a bit more explanation to expand on that.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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How to know one's way around for PhD in Covid times
I'm a physics graduate student in Italy, and I'd like to continue my formation with a PhD after my thesis.
I would like to consider the idea of going someplace, but the problem I encountered is I wouldn't know where. By this I mean, even in the field of theoretical physics, and even in the subfield of high energy theoretical physics, there are a ton of different research subjects and different teams researching them, even finding "the right one" among my professors was hard enough. How am I supposed to find one among many universities in many countries?
When I asked some friends who are doing a PhD away how they did it, they simply said "Oh I don't know, talking with professors and other students, listening to coffee-break chats, this kind of things". As you may guess, the past year (and possibly the current too) has lacked this kind of things.
How should one approach this problem in Covid times? Is there an online list of who is doing what in what university? Is it common to ask "what are your researchers doing" in an email?

You should ask your thesis advisor and other professors in your department for advice.

It's fine to email people.  Many funding agencies publish lists of grants awarded recently.

In my experience in finding a PhD, I would try two things: 1. Discuss your plans for a PhD with your thesis supervisors and ask them if they are aware of any relevant open positions or researchers you could email. 2. Search online for open positions or interesting research groups. Unfortunately, there isn't one place that lists all open positions etc., so you'll have to do a lot of searching online.

for theoretical (and especially high energy) physics look at https://inspirehep.net, under "jobs", plenty of PhD positions advertised there. I moved abroad for my PhD, and I found the position advertised on a similar website. Also, it sounds dumb, but I spent a lot of evenings looking up "[city] theoretical physics" on Google for many major European cities and ending up on department websites. I got some replies and interviews just by emailing random professors expressing interest.

I'd like to continue my formation with a PhD after my thesis.
You should have told us why. Think over it. Be ready to answer that question. Only go for a PhD if you can tell why. If you find your own answer convincing.
consider the idea of going someplace...
Excellent. Go to some place where research is decently funded, and where corruption is low. An easy measure for corruption is in the faculty's CVs: did they come from outside, or have they been promoted in-house?
even in the subfield of high energy theoretical physics
Don't choose the prefered subfield of some of the brightest minds, except if you are so brilliant that you can reasonably hope to make some difference in that field. Rather be the clever guy in a down-to-earth subfield. There are fascinating research questions everywhere. Lots of engineering is built on wild heuristics that is waiting for solid physical foundations. In condensed-matter physics, given the incredible variety of material structures, there are much more challenging questions than in theoretical HEP, and much less theoretists per question.
How should one approach this problem in Covid times?
Yes, times are hard. But I see there also a little advantage for your remote applications: as these days everybody has to use electronic means of communication, this brings you in one line with local candidates.
Is it common to ask "what are your researchers doing" in an email?
To whom would you write that mail? Wouldn't they get the impression that you haven't done your homework?
Is there an online list of who is doing what
Of course you can start from faculty lists, then proceed to their CVs and publication lists. But as you are not yet decided geographically, it's better you scan recent editions of high-level research journals, and look for work that is really impressing you. Then contact the authors.
in what university?
In many places, universities are second-tier. Don't overlook the well funded national laboratories and other research institutes outside the university system.

WRT Covid. Note that times are hard, indeed, but everyone else faces the same issue.

WRT field. I differ a bit. Do what you love. Don't try to be strategic about it if you wind up doing something you find boring.

@Buffy: Thanks. Edited Covid paragraph to acknowledge actual hardship. Regarding field: I'm eternally grateful to a HEP prof who did not accept me as a student ... I would not have gone far in that field.

Who can say. You may have done better than you think. I studied math because, mentally, I had no options (gave myself no options). It was great. But I had to switch to CS because of market conditions. I adapted as we all must. And I still think of myself as a mathematician, primarily.