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Summary: It took me 3 years to analyze data from one sample (n=1) whereas correct way to perform experiment would be with n>=3. Now my job and funding are in question, since I don't have more time. My supervisor pressures me to fabricate data.

I want to gather all the missing data but that will take another 3 years for same experiment; resulting in failure to timely submit internal evaluation reports, as well as termination of any fellowship. My supervisors are secretly suggesting me to somehow put some imaginary values in at this point, but that feels heartbreaking. So what is the best thing I can do now?

I am a Ph.D. candidate in soil sciences at a small academic university, where I have to do assays about different soil borne microbes such as an antibiotic assay, secondary metabolite, synergy etc. Now before starting my research, I was told to just ask the lab seniors and carry out the experiments. However, I did not have a simple and crisp understanding of biostatistics, and I was not told about what kind of statistics is applicable for my samples. I was tedious about the preparation of chemical reagents and buffers and I kept digging for historically important original papers that lack enough statistical analyses.But I missed that in the future, I will be required to submit statistical analyses that are up to the mark of current trends. I just kept doing simple experiments as shown in previous degree courses, without statistical repeats.

Honestly at this point, I am totally depressed. People all around me are pressuring me that if I do not fill up my dataset with imaginary data, my paper will not be accepted to any journal.

So what should I do in this scenario? I am extremely, severely depressed and go to therapy.

Update to answer questions asked in the comments:

  1. Locale: Some Asian country.

  2. Journals we choose: Very low impact, paid, costly. They prefer jargon rich language instead of too original language that describes how we reach to conclusion. If I write about a too detailed explanation, it would be double checked by my seniors, and at 'safest', they would be backspaced.

  3. Impact of reporting? From word of mouth, perhaps many other people at respectable positions here do (and did at past) this quite casually, and rivalry among them is intense. Except this incident, my supervisors were humanitarian towards me and many other people. Now at this point if I am selected by a rival employer (although that isn't going to happen without the explicit consent of present supervisors), there is no way the rival employers are going to trust a traitor.

  4. People are bloodthirsty for job and career (than actually learning), and they are immoral up to the point of stealing my personal stuff, hiding equipment, tearing journal pages to wrap glasswares, mouth pipetting broths risking own life. Period. Nobody is going to witness me. Nobody is going to get my back. Everybody will disagree with me.

I am under such a point where a PhD dropout seems more respectable than a finished PhD.

Update: Based on few more comments and answers:

  1. Yes I have considered of personal safety - I took a wait and watch strategy for a while.

  2. No, we don't have a statistics office. We have a stat department though, collaborating them might require consent from our department. I will consider this option. Calling the quality checking office is going to instantly identify me, and retaliation.

  3. In several other university departments where I studied at past, I have collected a lot of conflicts, again, without written evidence. It is not possible to keep the mobile recorder 24 hour on. Whatever, they are not going to issue me any recommendation letters. For sure.

  4. it is unlikely that I will zoom call or network. I have a very little motivation left. I want to choose some rather low paying career such as providing private tuition to children or to teach drawing or guitar or crafting, or to start a career as an animator. This will provide me more creativity and content. I would not require constantly evaluate the moods and intents of others. In future if a new system appears where people spontaneously contribute to research, maybe I will consider that in old age. I am happy with little, and that is enough for me.

  5. I would suggest the scientific community to develop some other technique to prevent or detect this type of situations. Make it mandatory to submit all photographic/ videographic evidence for each and every claim. Make it mandatory to submit each mathematical details, procedures to making mixtures, etc. Also publishing behavior can say a lot. Especially a prolific amount of publication may be a red flag for predatory behaviour.

  6. System needs a lot other change. Do not mandate Ph. D. for jobs where Ph. D. level skills is not necessary. Also make an outlet for partly incomplete informations to be published, with intellectual properties associated to the students; and unlike peer review by a few referees, let it be reviewed by entire world. let them make comments and collaborations.

Update:

  1. I talked to superviser and made it absolutely clear that under no circumstane I will execute that suggestion. They agrees to it, sort of. They tells me to focus on "actual work" of pouring and mixing all those, than neatpicking too much with all those theoretical stuff.

  2. I am seeing this type of misconduct as more of a systemic fault than individual fault, because lot of people talk about 'putting some values' quite casually and are achievement-thirsty. I maintain a distance with this kind of people because every time they makes me feel ghosted, ignored, ashamed of not completing the curricula 'as intended', or blameshifts when I try to do anything new.

  3. Looks like the Ph.D. memes on social media indeed reflects a lot of unreported truth. Recently I have found a meme regarding n=1 back from 2019 that also indicates somebody else also made a similar blunder.

  4. I have also heard, some of our very reputable professors said the ethics curriculum is very "boring"... that reflects they don't know to teach it an insightful way.

  5. I met a very senior scholar (who is now in no means doing research or academic job though) said me once that there is credit in filling up with data because it takes "knowing the work better than others" so that "only naive people are caught".

Small update regarding demographics: Our former state education minister, currently one of the supreme power, just caught by detectives for laundering money worth approx 300000 USD (at least) while investigating a huge school teacher recruitment petitions and protests. A similar university like us, known to award him a fake Ph. D. degree as well other professors are hiding their opinion about this education minister's Ph. D. guide, and the responsibility is being pushed solely towards his guide. But we all know the rapport and networking among all those professors. This is the level of corruption in the authority to whom we are expected to "report" something.

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    Echoing the first two answers, faking data is the absolute worst possible thing you can do. It is the path to career suicide. It will possibly come out and maybe at the worst possible time.
    – Buffy
    May 23 at 16:07
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    And, for the record, you seem to have been given bad (really really bad) advice for a long time.
    – Buffy
    May 23 at 16:09
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    I'm going to doubt that. I once had a poor advisor and couldn't see it for a long time (until I escaped) for lack of experience.
    – Buffy
    May 23 at 16:16
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    You are, presumably, a free man or woman with moral agency of your own. You've ended up in an unpleasant situation through what seems to be the fault of your supervisors, but note that whatever you do in this situation will be your own decision and you will not be able to blame it at some later point on your "shortsightedness" or "gullibility" or someone else pressuring you into it. May 24 at 18:13
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    >As well please let me know the consequences of this kind of misconduct Two words: Elisabeth Bik May 24 at 19:16

12 Answers 12

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  1. Never fabricate data under any circumstances. That is the worst form of academic dishonesty there is, and is unforgivable. The scientific process depends on our ability to build on the work of others, and we can't do that if we can't reasonably trust that work. (Honest mistakes happen, as do poor studies. But if a flawed study is written up honestly, we can judge how much we should trust the results based on the experiments described. We can't do that if the paper contains lies.)

  2. Report your supervisors immediately. They are terrible people and need to be removed from the scientific community as soon as possible to limit the damage they can do in the future and begin mitigating the damage they've already done. Possible people you could report to include the department chair and the editors of any journals they've published in. Try to get written evidence of the suggestion first, but if you can't, you should still warn the chair so people can keep a closer eye on them in the future. The scientific community is being actively harmed as long as they remain within it.

It sucks that you've found yourself in this situation. If you don't know the chair well, you might want to talk to other professors who you trust (preferably tenured) to get some help dealing with this. Having someone with real clout backing you up could make it a lot easier. One good aspect to come of this: if you successfully uncover people who are fabricating results, you will have made a positive contribution to science by having done so, and will earn the respect of anyone whose respect is worth having.

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    -1: While I fully agree that fabricating data and selling it as genuine is the worst possible approach, the second part of this answer may seriously backfire. Chances are good that the advisor has a better connection to the department chair than the OP. Also, sending seemingly random emails to journals that their articles are fake is something the department chair certainly won't like either and will escalate. Especially when done either before or in parallel to talking to them. The other side of the story will sound like: The OP cannot finish their PhD in time, now they are panicking.
    – VoodooCode
    May 24 at 17:24
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    @VoodooCode It might backfire. It still needs to be done if we want to avoid having fabricated results in the literature. The emails to editors can be done anonymously, if the OP chooses. It probably isn't practical to inform the chair anonymously, but the only way the chair can hear about this and not be enraged that people in their department would suggest such a thing is if the chair is just as unprincipled as the supervisors. And on the off chance that the department is that rotten, the OP's PhD is probably worthless no matter what they do.
    – Ray
    May 24 at 17:32
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    Comments are not for extended discussion; this conversation has been moved to chat.
    – cag51
    May 26 at 18:17
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This sounds more the fault of your advisors than yours for not equipping you with the supervision you need to perform your experiments up to standards.

Do not under any circumstances falsify or misrepresent simulated data as actual data.

Your advisors should help you come up with a solution for moving forward, with the understanding that their own lack of supervision has caused this problem. If their suggestion is to falsify data, well...that tells you everything about their ethics. I would feel that a PhD earned working with those people is worth the same amount as that fake data is. Hopefully you have misunderstood and this is not what they are suggesting. If they are suggesting this, then I have to say they have much more to lose than you do, and if you have documentation of this suggestion and this documentation is shared with the university administration or the public, those supervisors are going to be in quite a lot of trouble.

From a statistical perspective, I do not think the scenario as you describe is as fatal as you make it sound. There may be a standard in the field that samples are collected in triplicate, but nothing about statistics prevents you from analyzing data that are not taken in triplicate. It may be that you do not have enough samples to perform a sufficiently powered null hypothesis test, but performing triplicate sampling doesn't actually give you more samples, it just refines your estimates a bit. I assume you have more than just 1 sample per group total, though; if not, then it seems very unlikely that even 3 would be enough.

Not every project in soil science requires 3 years to complete. Do a version of your investigation that can be done in 1 year. Work with your advisors to figure out what this would look like. Use the work you've already done to make this an efficient 1 year. It's normal in science to have to throw away years of work; often, the time elapsed is about learning the methods and once you know what you're doing it can be performed quickly.

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    @FrustratedBird Can you clarify how many total samples you have, and how many different groups/conditions?
    – Bryan Krause
    May 23 at 15:45
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    Consult with someone that has a good handle on statistics that you can share your situation with, and get their advice. Based on what you've shared here, I can't suggest anything specific for your next steps. Get advice in writing from your advisor about what you should do next.
    – Bryan Krause
    May 23 at 15:55
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    @FrustratedBird Not every project in soil science requires 3 years to complete. Do a version of your investigation that can be done in 1 year. Work with your advisors to figure out what this would look like. Use the work you've already done to make this an efficient 1 year. It's normal in science to have to throw away years of work; often, the time elapsed is about learning the methods and once you know what you're doing it can be performed quickly.
    – Bryan Krause
    May 23 at 16:02
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    @BryanKrause OP's issue is basically a variant of the common issue described in Replicate or lie. I agree with Prof. Prosser: what OP describes is indeed a fatal issue from a scientific perspective. OP can still fix this but the fundamental issue is as serious a scientific issue as it gets.
    – Roland
    May 24 at 5:59
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    @FrustratedBird If you make up data, you're essentially contravening the entire point of being a scientist. Not only would you be lying for personal gain, you'd also be hurting human knowledge as well, because it'll take time for people to realize that your data is nonsense. And it won't be pleasant for you when they do. May 24 at 15:51
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Following up on @BrianKrause 's excellent answer.

Do not present fake data as real.

You might be able to write something useful and interesting with the data you have along with several different invented or simulated data sets that you properly identify as such. Can you discuss the consequences for your work under different assumptions about what the data you do not have might show?

Your advisors might (should?) accept this kind of compromise as an apology to you for failing to instruct you properly from the start.

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  • Thank you very much. I want some more information about consequences of imaginary data and if it is possible to spot these data from real one. May 23 at 16:01
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    @FrustratedBird Yes, it's possible. No, you shouldn't try to defeat those methods. Just stop even considering it. Every person who has discussed faking data with you will know your data is fake and any one of them can turn at you at any time for any perceived slight.
    – Bryan Krause
    May 23 at 16:03
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    Thank you so much for encouraging me to keep sanctity of my work. I have too many dreams and at this moment i feel like end of world May 23 at 16:07
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    There was the infamous fake study about Ivermectin vs. Covid, where someone did a purely statistical analysis of the supposed study and was able to prove that a small dataset was duplicated several times. And once that this statistical fraud was found, they looked at the original data closely and found they were also faked; that's something that wouldn't happen to you.
    – gnasher729
    May 24 at 11:06
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I'm also going to point out that if it took 3 years to do the experiment, then assuming you're researching something useful then the data is massively valuable and you need to get it written up. Someone else can always spend another 3 years adding to the sample size (or possibly, if you have learnt things which could make it faster and you document them, less time than that), but your "initial" results are still valuable. Just make sure you document the experiment such that its repeatable.

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    One possibility: Developing a method to measure that particular kind of soil samples may be publishable as method paper, where the emphasis is on the (wet lab) method developed to be applicable to, say, a particular type of soil, rather than on finding out properties of the particular soil. In some cases, I'd say that one can even publish on how to implement (= get working) a particular, new method in another lab. (FWIW, I'm analytical chemist) May 26 at 18:36
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I will answer this from a statistical viewpoint and omit any criticism of your supervisors.

You have n=1 but claim to need n=3. I assume this is a consequence of some statistical tool, which simply cannot be used at all for n=1. I further assume this is due to a frequentistic approach in this tool, i.e. parameters found by this tool are direct functions of the data. With this default approach, it would be indeed impossible to come to statistical conclusions, but by altering your approach, we can remove this constraint.

Specifically, we move from a frequentistic to a bayesian approach of your statistical analysis. For this, we place meaningful prior distributions on each parameter in your statistical analysis. This makes it possible to infer solutions from n=1. Your solutions are then represented as a posterior distribution, which represents the uncertainty in each parameter. This uncertainty will be high for n=1, assuming your prior distribution is broad enough, but the solutions are perfectly valid from a statistical viewpoint.

There are two drawbacks: First, the mathematics can be difficult. It might take you a few weeks or even months to understand this approach and implement it correctly (but this should be better than 3 more years of experiments). Second, picking decent prior distributions can be difficult. By definition, you can pick prior distributions in a way such your solutions will tell whatever you want. This is a bad approach and people will criticize this. Instead, you have to find 'what-makes-sense' in your application and what other people can agree on. This can be very tough, but, as a domain expert, you are in a good position to find such priors. Good luck!

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    Fellow statistician here. I upvoted your answer, but to be honest, either $n=1$ or $n=3$ sounds like a horrendously too small sample size. Yes, one can possibly salvage something using a Bayesian analysis. Nevertheless, it would have been useful to consult with a statistician before the experiment was even planned. I know, that is not useful at this point in time... but it may help others in the future. Recall R. A. Fisher's quote about consulting the statistician after the experiment. May 24 at 13:19
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    @StephanKolassa yes, you are absolutely right. My answer is made on the premise of fully accepting the described circumstances. May 24 at 14:14
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    I think that this answer is the best that can be salvaged from the existing sample data. And for God's sake find someone outside the influence of your current advisers to advise you on this statistical approach. Obviously there needs to be a recommendation in your final write-up for a greater number of samples. No only for reasons of confidence in the final estimates but also to evaluate the efficacy of your Bayesian analysis of singular samples.
    – Trunk
    May 24 at 18:30
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    @StephanKolassa: FWIW, in a comment to Bryan's answer, OP writes, "I wont tell actual number of samples here but they are within 10. Now I require to make bar graphs annova students-t etc.". So I read that as "1 group with ~10 individual samples". Given that inferences for a mean (like student's t-test) usually recommend a minimum size of around 30, maybe advisers are saying "you should have 3 groups of that size". (And possibly OP's first language is non-English.) May 24 at 19:33
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    @Daniel R. Collins If that's what he's got, he'll be fine. I think Student-t for population mean will run with reasonable precision with sample size of ~ 5. n = 30 is the textbook cut-off for people want to apply Normal stats to the sample mean for "well-behaved" underlying population distributions. But OP can estimate pop variance using Chi-squared distribution if the population is distributed with reasonable "Normality". Anyway he'll have to get pro advice on all this stuff as he reads up on it all.
    – Trunk
    May 24 at 23:50
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The experience you gained in this 3 years is invaluable and it is written nowhere that everybody has to finish their PhD in 3 years. I am not saying this to downplay you, but jus to state the obvious: unrealistic expectations can destroy the mental wellbeing of a person and it will lead to unethical practices (sorry for stating the obvious, and I know that most likely in your culture all these words are just empty words ... they are empty words even in many of the progressive, western mighty countries).

My supervisors are secretly suggesting me to somehow put some imaginary values in at this point

You have to clearly stand your ground. Explicitly tell your supervisor that you will never produce values that were not measured in the lab. Your supervisor must provide you funding to repeat the experiments. This means fundings for 3 additional years must be secured.

Since this is going to be unlikely, that request must be your "ice-breaker" to fight to get funds for at least 1 year and start the needed sampling for statistical repeatability. Additionally, you are blocked with your supervisor, but you must defend your integrity: data cannot be faked (not even at a Bachelor degree level). You have to fight because he has to find a way for you to publish, or at least to defend your thesis ("how sure are you about your results?" "our interpretation is supported by the experiments performed in the previous years, experiments are being repeated and when results will be available we will publish in an international peer-reviewed paper).

tl;dr

You have a crappy supervisor. Both at the intellectual and moral level. It is not your fault. Either you win their support and bring this PhD to an end, or you have to start it again in a different institution/country. With your experience, re-starting a PhD will be an experience similar to half a post-doc. No person is equal to the other, but no one will judge you because it took you 3 years at an institution and 3 years at a different one to complete your PhD.

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    In my country, there are multiple ways where one can do a PhD without receiving funding from the supervisor, and there are circumstances in which it would be legally impossible for supervisors to provide funding beyond a particular point. Without knowing anything about the system in which the OP is working, I think it's not helpful to claim that the supervisor "must provide" funds for three additional years (and while I'm at it: the parenthetical at the end of the first paragraph makes assumptions about the OP's culture which I find a bit... problematic to be honest).
    – Schmuddi
    May 24 at 16:58
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    @Schmuddi it is not an assumption: unless you cannot properly read OP post, it is obvious OP is pursuing a PhD in a non-western country. For such cases, even in your unnamed german-speaking country a supervisor can find the money, maybe formally transferring you to a research institute nearby outide of the university and the like.
    – EarlGrey
    May 24 at 16:59
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    I think I can properly read OP post, thank you very much. I don't think that this subtle ad hominem was very productive for this conversation, so let's be blunt: You claim that you have enough information to be certain that the OP comes from a culture in which talking about unethical practices as the consequence of unrealistic expectations is "just empty words". I consider this claim presumptuous and unacceptable for academic discourse.
    – Schmuddi
    May 24 at 17:06
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    @Schmuddi please read the update from OP. And please wake up, there is an horrible world outside the western world (and in the western world it is already rather ugly, see famous scandals in the US, such as Marc Hauser, wihtout forgetting the large hidden numbers, as per in the Netherlands nature.com/articles/d41586-021-02035-2 where aggressive practices imported from abroad conflates with local bad practices...)
    – EarlGrey
    May 25 at 4:14
  • "And please wake up". You're a funny person indeed.
    – Schmuddi
    May 25 at 5:11
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  1. Quite obviously, most people suggesting answers in this thread are coming from the western culture and severely underestimate the consequences for you in the case they report their advisor at the point when their career is going through such a rough moment.

    Fighting and reporting advisors could be postponed until you are in a safer position. I would suggest this path only if you had enough material for the defense, and they were clearly failing you, which apparently is not the case. Also, in such a situation you would be unlikely to get a reference letter.

  2. You should 100% go to therapy independently of how the story evolves. Therapy is a resource, it's a new empty room in your house where you can relax, get support, and maybe even figure out what to do with the rest of your house. Also keep seeking for support besides therapy. Asking a question here was a right step.

  3. Honestly ask yourself whether you are still interested in pursuing career in science. If not, there is no point in fighting for PhD. And there is absolutely nothing wrong about such a choice. Do whatever makes you happy, self-content, etc. Taking a break could also be an option.

In what follows I will assume that you want to stay in academia.

  1. — What do all great scientists have in common?

    — They all had a great advisor.

    You advisors are clearly trash, from both scientific and humane perspective, and I can hardly imagine that you will benefit from interacting with them.

  2. If I could give just one piece of advice to young scholars, it would be "find an advisor who is a decent human, i.e. the one caring about his students". How is this applicable to your situation? One option I can suggest would be restarting / transferring to a different PhD program after you find a new advisor. I know people who did this after 2-3 years of PhD, I did this myself after 5 years, and I have never regretted it.

I don't know if you have any circumstances completely eliminating such an option, so I will assume that you would consider it.

  1. Advisor search process tuned to your case:

    • Look up people in your field and find those whose ex students have nice careers, ideally in universities of higher rank than where they did PhD.
    • Once you have a shortlist, contact them directly, asking for a zoom talk asap. You need to explain your whole situation and see if they may be interested in you.
    • Try to talk to professor's current students.
  2. At this point, you may feel like you are a loser carrying the burden of bad experience. **But you are also a tough one!

    • You have quite a lot of independent (with little support from you advisors) research experience;
    • You have more knowledge than a fresh PhD candidate;
    • You are clearly passionate about your science, if you are willing to take the risk of devoting to it more of your lifetime after what happened before.

Who would not want to get such a PhD student???

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  • +n! for wonderful insight into the situation and really good advice. This too shall pass, and it will make a wonderful story to tell some day.
    – uhoh
    May 26 at 13:37
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Under no circumstances should you submit fake data. You have data that is useful, that took a long time to collect, and even if your methods were not perfect, you wouldn't want to throw it away.

You could try to learn exactly how bad having one repeat instead of three repeats is. Add this information to your paper. It will likely improve how your data is viewed (you may be able to show that your data is not perfect but still useful) and that information on its own is valuable as well.

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Comment: Sorry, I don't have enough points to post a comment. But I'd like to suggest that you should definitely get and read this book...

"Stealing Into Print:   Fraud, Plagiarism, and Misconduct in Scientific Publishing"
https://www.ucpress.edu/book/9780520205130/stealing-into-print

where you'll see that the two most common (among many other) unethical academic publishing practices are (1)fabrication and/or falsification of data, and (2)plagiarism. It also goes into several hundred pages of detail about the issues troubling you. But my (somewhat dim) recollection of that material is that it won't really resolve these issues for you. However, I hope that maybe it'll help you to see that they've been very carefully thought about, documented, and discussed.

P.S. Hmm, I wonder how much of that book is completely honest ( sorry, couldn't resist :)

  E   D   I   T
In reply to the comment "your answer is unclear" below, the op seems torn between the alternatives of (1)behaving in what he feels is an unethical manner, or (2)abandoning his career path. I'm suggesting that, although the book ain't gonna resolve that dilemma for him, it nevertheless provides numerous real-life case studies and in-depth discussions of them. And all that may help the op put his situation in context and in perspective. For some of those cases discussed in the book (as I recall them), it's pretty much similar to the op's "publish or perish" situation. Whereas for others, they're just looking for additional grant money, and don't mind cheating to get it. And lots of cases in some kind of in-between situation. So there's a whole spectrum of ethical-->unethical. And maybe the op can reconcile his "Sophie's Choice" by realizing there are many more people willfully choosing to do many worse things than the choice he's inescapably faced with.

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    As it’s currently written, your answer is unclear. Please edit to add additional details that will help others understand how this addresses the question asked. You can find more information on how to write good answers in the help center.
    – Community Bot
    May 25 at 17:06
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Yes, fake data is anathema to the very essence of science.

However, you are a lone crusader with next to no power struggling to survive in a completely corrupted environment. Noble ethics is not going to help you.

"Virtue is its own reward - it has to be!"

You have my most profound commiserations. Maybe it is because I am getting old, but it seems to me that more and more of academia is sinking into this morass of cut-throat careerism and "who gives a f*ck nobody is going to read this closely anyway"

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I've worked in several countries in Asia and I think all the advice about collecting incriminating evidence about your supervisor or reporting them will probably not be helpful in your case. If an issue comes up now or in the future, everyone will work to ensure the survival of a professor and will not think twice about throwing a disposable student under the bus.

All the advice about not ever faking data is gold. You won't be able to live with yourself and if it does come out later there will be a bus waiting for you to get thrown under.

Hang in there; this too shall pass and as @ mavzolej points out you will be stronger and more experienced when it does.

Welcome to Stack Exchange!

There are almost 200 different sites to choose from and one of them is Statistics!

Perhaps this is an opportunity to join a second site and discuss the statistical situation that you are in in more detail. Have a look around that site to see what question asking looks like and then start asking questions.

  • Is there any way I can say something of significance with n=1?
  • Is there some subset of the work I can do instead of going all the way to n=3?
  • Are there alternate analyses I could try? etc.

You can link back to your question here if you want to add some background but mostly just explain your statistical situation.

You can probably find a way to anonymize yourself there (it's a popular site!) and no need to mention soil science or link back here if you don't want to.

Use this as an opportunity to possibly learn some new statistics and to learn more about Stack Exchange - it can really be incredibly helpful in many ways.

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    apart from 200 stack exchange sites, OP has a stat department in their uni. I would first try to get in touch with PhD/Master students there, unofficially. OP may find out they are all PhD-thirsty students not caring about moral issues to get theri PhDs, or OP may find someone like-minded, who knows, sometimes the luck plays in favour of one's life....
    – EarlGrey
    May 27 at 9:39
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    @EarlGrey not every "small academic university" in Asia will have an actual department of statistics and it's often much, much harder to just go knock on someone's door (or the email equivalent) as a stranger and just ask a question compared to western style academy. The implications of saying something like "Hey I need some help a problem I'm working on" to someone you don't already have a research relationship with are usually quite negative while in the west it can be a perfectly reasonable thing to do.
    – uhoh
    May 27 at 15:13
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    "We have a stat department though" OP's words. Then, I understand the difficulties in talking to other PhDs/researchers/etc. Not easy. If there are no social groups, or at least the understanding that all PhDs are in the same boat (full of ... water), one really need a lot of luck to get in touch with someone supportive. That's why I stated "OP may find" in my words there is a lot of hope and the understanding that a lot of luck is needed, unfortunately.
    – EarlGrey
    May 27 at 16:03
  • @EarlGrey Oh indeed it they do! Yes, excellent advice, all.
    – uhoh
    May 27 at 19:18
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Regarding your PhD keep in mind a PhD is a learning experience. What you have to demonstrate are not results, but the ability to investigate. Demonstrating that something is wrong or explaining you did not get the expected results is a valid thesis. Just make extra emphasis in the state of the art and why your approach won't work. This will help some other experiment with similar properties to avoid losing 3 years due to poor experiment design.

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