Sample Cover Letter for Journal Manuscript Resubmissions

By Roy F. Baumeister

Dear Sir, Madame, or Other:
Enclosed is our latest version of Ms # 85-02-22-RRRRR, that is, the re-re-re-revised revision of our paper. Choke on it. We have again rewritten the entire manuscript from start to finish. We even changed the goddamn running head! Hopefully we have suffered enough by now to satisfy even you and your bloodthirsty reviewers.

I shall skip the usual point-by-point description of every single change we made in response to the critiques. After all, it is fairly clear that your reviewers are less interested in details of scientific procedure than in working out their personality problems and sexual frustrations by seeking some kind of demented glee in the sadistic and arbitrary exercise of tyrannical power over helpless authors like ourselves who happen to fall into their clutches. We do understand that, in view of the misanthropic psychopaths you have on your editorial board, you need to keep sending them papers, for if they weren’t reviewing manuscripts they’d probably be out mugging old ladies or clubbing baby seals to death. Still, from this batch of reviewers, C was clearly the most hostile, and we request that you not ask him or her to review this revision. Indeed, we have mailed letter bombs to four or five people we suspected of being reviewer C, so if you send the manuscript back to them the review process could be unduly delayed.

Some of the reviewers’ comments we couldn’t do anything about. For example, if (as review C suggested) several of my recent ancestors were indeed drawn from other species, it is too late to change that. Other suggestions were implemented, however, and the paper has improved and benefited. Thus, you suggested that we shorten the manuscript by 5 pages, and we were able to accomplish this very effectively by altering the margins and printing the paper in a different font with a smaller typeface. We agree with you that the paper is much better this way.

One perplexing problem was dealing with suggestions #13-28 by Reviewer B. As you may recall (that is, if you even bother reading the reviews before doing your decision letter), that reviewer listed 16 works that he/she felt we should cite in this paper. These were on a variety of different topics, none of which had any relevance to our work that we could see. Indeed, one was an essay on the Spanish-American War from a high school literary magazine. The only common thread was that all 16 were by the same author, presumably someone whom Reviewer B greatly admires and feels should be more widely cited. To handle this, we have modified the Introduction and added, after the review of relevant literature, a subsection entitled “Review of Irrelevant Literature” that discusses these articles and also duly addresses some of the more asinine suggestions in the other reviews.

We hope that you will be pleased with this revision and will finally recognize how urgently deserving of publication this work is. If not, then you are an unscrupulous, depraved monster with no shred of human decency. You ought to be in a cage. May whatever heritage you come from be the butt of the next round of ethnic jokes. If you do accept it, however, we wish to thank you for your patience and wisdom throughout this process and to express our appreciation of your scholarly insights. To repay you, we would be happy to review some manuscripts for you; please send us the next manuscript that any of these reviewers submits to your journal.

Assuming you accept this paper, we would also like to add a footnote acknowledging your help with this manuscript and to point out that we liked the paper much better the way we originally wrote it but you held the editorial shotgun to our heads and forced us to chop, reshuffle, restate, hedge, expand, shorten, and in general convert a meaty paper into stir-fried vegetables. We couldn’t, or wouldn’t, have done it without your input.

Sincerely,

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Shit I learned during my PhD

Jon Tennant just finished his PhD in paleontology. This post originally appeared on Jon’s blog, Fossils and Shit. Follow him on twitter @protohedgehog.

Doing a PhD is one of the greatest trials you will ever experience in your life. It is physically and mentally grueling, you will be challenged and pushed to the limit every single day, and the pressure levels are so high they will bust you right into the sixth dimension if you’re not prepared or strong enough.

So yeah, they are not for the faint of hearted. That is, if you want to succeed by pushing yourself to the limit, excel in everything that you apply yourself to, and grow to become more powerful than you can possibly imagine (compared to the wimpy undergrad you used to be). But I imagine you wouldn’t even be doing a PhD if this wasn’t your mentality anyway.

I’m a strong believer in committing yourself fully to something if you believe in it, and doing everything within your power to achieve your goals. A PhD is basically a 3-4 year long single project that you can, and should, dedicate yourself too. Now that I’m nearing the end of my own challenge, I wanted to share some simple things with you all that might help in some way.

1. Don’t compare yourself to other people, especially researchers
Every day, you will see other people achieving their own things. Encourage the success of others, but do not think that this means your own work has any less value. I think this competitive nature of academia is one of the main causes of Imposter Syndrome for researchers. Acknowledge that others will succeed, and that your own successes will come too. Which leads on to…

2. Be content with your successes
Celebrate all the things! Get a paper published? Awesome! Abstract accepted for that conference? You’re amazing! Get some code to run? Get a beer! Accepting that your successes, no matter how big or small, are meaningful is a great step towards acknowledging your personal worth. Both to yourself and others. That doesn’t mean rub them in other people’s faces; simply allow yourself to enjoy the feeling of completing something that meant something to yourself or others. It took me about three and a half years of my PhD to get there and realise ‘Oh. Maybe I’ve finally done something good.’, and then it was like a cascade from there where every achievement began to mean something and excite and motivate me even more. My only wish is that I’d realised this sooner.

3. Social media is a doubled-edged sword
Social media such as blogging and Twitter are amazing to learn for personal development, networking, and science communication. The negative side of this is that social media emphasises point 1 in this list. People basically pump out all of the good things in their lives, and it’s like having 1000 marginally interesting success stories pummeled into your face on a daily basis. That is not healthy, as it becomes too easy to see this as a single timeline of success that you could not possibly live up to. This is why it is so essential to know that if you do use social media, what you’re looking at is a multitude, and not a single narrative of another person’s life.

4. Challenge everything, especially that which seems normal or is the status quo
Universities are places were freedom of thought and freedom of expression are standard. Note that this doesn’t mean you are at liberty to be a dick, and simply do or say things without thinking them through. If someone tells you to do something ‘because that’s the way it is’, challenge it. Conforming to expectations is not only boring, but changes nothing. Research and academia are places to unleash yourself and your creativity in ways that you will never get in a standard workplace, and you should embrace the opportunity. Rules are meant to be broken.

5. Having a relationship during your PhD is insanely difficult
A PhD is so time consuming it’s ridiculous. When people say they work 45 hours a week, a PhD student replies “Oh it must be nice working just a part time job..” This impacts quite a bit upon the hypothetical ‘work life balance’. I’m not gonna sugar it up, there is no work-life balance. Work becomes your life. Even when you’re not working on your PhD, you’re thinking about it. Trying to reconcile this with a love life is insane. If you can find a significant other who understands this, keep them for life. If they don’t, it’ll make the relationship all the more difficult. It’s not about placing one person/thing above another, but recognising that at certain times there are certain priorities that have to take precedent.

6. Take every opportunity to travel
PhD students can be blessed with unparalleled chances to roam the planet. We get to attend conferences, workshops, talks, and do our research in some of the most exotic, weird, wonderful, and exciting places on the planet. Embrace this chance, as you probably won’t get it ever again. Never be afraid to try something or somewhere new, and embrace every opportunity as a new learning experience.

7. Take every opportunity to learn
A PhD is a learning experience. Don’t ever feel stupid for not knowing something – no-one knows everything, and the whole point of research and education is that we’re forever pushing our boundaries by discovering new things. What is obvious to some people is clearly not to others, and you should not be afraid to ask questions, or be made silly for asking them. Over the course of 3-4 years for a PhD, you will be constantly learning new things, expanding your knowledge horizons, and acquiring new skills. Some times, you won’t even recognise that you’re picking up or developing skills. Often it’s worth going out of your way to try new things: a foreign language or a new coding language, creative writing, yoga, art, baking – anything that helps you to enhance yourself.

8. Use your spare time to learn ‘secondary’ skills
By ‘secondary’, I refer to those which are not strictly to do with research. These include learning how to write for non-specialist audiences through blogging as a form of science communication, learning marketing, advocacy and community building skills as a form of networking and promotion, and social media usage in order to more effectively communicate with a diverse range of audiences. These skills are invaluable and can open up a multitude of new opportunities, and if you learn to integrate them into your daily workflows can become valuable extensions of yourself.

9. Learn to code. For the love of God learn to code
Coding is frickin’ difficult, don’t let anyone tell you otherwise. Some people have a knack for it, others don’t. In the modern age though, the ability to code, or at least read or execute code, is so damn important. I’ve only learned how to use a bit of R during my PhD, but this basically saved my research just by learning the basics. Websites like CodeAcademy are super duper useful for picking up coding skills, and good fun and free too.

10. Some people are assholes, and there’s nothing you can do about it
The common asshole can often be a difficult species to find. Common traits include: 1. Talking about others negatively behind their backs; 2. Only ever talking about themselves and their activities; 3. Interrupting you to tell a story that’s just oh so much better; 4. Poisoning the way you think and act so that you begin to question yourself, but not in a good way; 5. Sapping all of your time and energy to deal with them and their problems; 6. Taking everything from you, but never giving something in return. One thing I’ve learned is not to engage with people like this. People who are not helping to build you are not people to surround yourself with, and are best removed swiftly and painlessly from your life. This also accounts for serial harassers, some times even people under the facade of ‘close friends’, and those who refuse to be held accountable for the words they say and the actions they perform. There is a whole world of amazing people out there, and do not settle for people who act like shit and treat you like anything less than you deserve.

11. Be there for other people as much as you can
I might have mentioned this once or twice, but doing a PhD is fucking difficult. Some times, those most in need are those who hide it most. Learn to read the signs of when people might be struggling, and be there as a stone pillar for them when they need it most. This is just part of being a good friend, and some times for people simply knowing that someone is there for you can make all the difference.

12. Don’t sacrifice your mental or physical health for research
At Imperial College, almost every grad student I know was suffering from some sort of mental or physical health issue. Alcoholism, depression, anxiety, stress, insomnia – the list goes on. The pressures of academia are insane, and don’t kill yourself just to get out a paper or do another experiment. Everything that needs to get done will get done with time. You work more efficiently by pacing yourself. Staying healthy in body is also a path to staying more healthy in mind. I started running during my PhD, and found that after a while I was able to focus more, sleep better, and not be so damn exhausted all the time.Also, don’t over-caffeinate – quitting ten shitty cups of coffee a day was awesome, I gained the ability to think again. If you’re a coffee fan, have one or two a day strategically. Drink a glass of water in the morning as soon as you wake up, and stay hydrated during the day. Don’t binge on carbs, and try and have a healthy diet. This shit actually works, is ridiculously simple, and you’ll feel a positive difference. Meditation can also be a powerful method for clearing your mind and helping you to focus – apps like Headspace are great for starting with this.

13. Publish the shit out of your PhD as much as possible
I’ve written about this one already here.

14. Learn how to empathise with others
This is a ridiculously powerful way of thinking, and very difficult to grasp. I’m not sure I’ve got it yet fully, but is something I try all the time. My parents always used to say to me ‘treat others how you would like to be treated’, and being able to place yourselves in the shoes of others and understand their feelings is important for this. By doing so, you’ll be able to understand the problems of others more easily, and generally perceive everyday issues in life from a more enriched diversity of views. It also means that you’re not thinking about things selfishly, shallowly, or narrowly. I got sick of people in London being so self-centred about their thinking, when it came to personal and academic issues, and is actually one of the key reasons why I left London and Imperial College in the first place.

15. Learn how to think about problems from a range or perspectives
Problem solving is an intrinsic part of academia. Shockingly, problem solving is not easy, either to do with research or real life situations. Being able to consider problems from as diverse a range of perspectives as possible is a very powerful tool for understanding and resolving them. Learn to be solution-oriented, focus on the positives, and consider how other people are perceiving a situation and what the implications of this are. Follow thoughts and actions through like a web – consider all possibilities and all implications of this. Through this, often the optimal solution will emerge, and you will be braced for all possible outcomes.

16. If you recognise that you have a weakness, do everything you can to overcome it
Part of self-development is recognising that you are not perfect. Everyone has weaknesses, or parts of themselves that can improve – if you can’t think of anything, think harder, or stop being so arrogant. Learning what these traits are is the first step towards building upon them. For example, if you have an issue with approaching new people and initiating conversation, slowly build up your confidence in smaller steps by approaching groups, people who you know from social media, or by planning out the first few lines of a conversation in your head in advance. For every problem, there are a thousand solutions – you just have to find that which suits you the most!

DISCLAIMER: These views are based on my own experience, and might be utter bullshit.

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25 PhD Feels All Doctoral Students Have

Mairi Young is a PhD student at the University of Glasgow, researching why people are scared of the dentist (sort of). She is also a foodie and self-confessed junk food lover, blogging over at The Weegie Kitchen.

  1. Having to explain to your Mum, for the fiftieth time, no you’re not writing an essay.
  2. Having to explain to your Mum, yet again, that a Viva is not just an exam.
  3. Having to explain to your Mum a PhD is a real job.
  4. Asking your Mum to just stop asking about your PhD.
  5. Sneaking out Leaving the office at 6pm and feeling guilty.
  6. That twinge of guilt over the sheer amount of paper you print on a weekly basis.
  7. Feeling sad that you’ve single-handedly destroyed a rainforest by doing a Systematic Literature Review.
  8. Bringing your laptop and papers home for the weekend/holidays/trip abroad (tick all that apply) but never actually opening the bag and feeling its judgmental glare the entire time so you can’t fully relax.

    Source: PHD comics

    Source: PHD comics

  9. Batch cooking on a Sunday for the week ahead and feeling like you have won at life because you’re so organised.
  10. Eating microwaved lasagne for lunch and dinner for the 4th day running and wondering why you ever thought batching cooking was a good idea.
  11. Quietly loathing the postdocs who can afford fancy ready meals for lunch.
  12. Hating compulsory seminars.
  13. Attending compulsory seminars because offer free sandwiches and it’s an escape from microwaved lasagne for the 5th day running.
  14. Stocking up on free sandwiches at free seminars.

    PHD Comics

    Source: PHD Comics

  15. Feeling flush when you buy prosecco from Aldi.
  16. Eating crisps in the office by placing each crisp on your tongue and patiently waiting for it to dissolve because you don’t wanna be that person.
  17. Feeling super smart when you use words like epistemology and ontology.
  18. Feeling like a dunce when you have to explain the meaning of these words.
  19. Writing your acknowledgements page and wiping away a tear because it’s very Gwyneth Paltrow at the Oscars circa 1999.
  20. Watching as your office uniform goes from suit jacket to hoodies swiftly in the final six months (or the first six weeks).
  21. Its 3 months till completion and you can’t remember the last time you ate a vegetable.

  22. Applying for post-doc positions with a 37.5-hour working week and realising (very soon) you will no longer have to work an 80-hour week.
  23. Daydreaming about all the productive things you’ll do with these extra 40 hours a week.
  24. Realising you’ll probably just use it to catch up on sleep and your laundry pile.
  25. Realising that postdocs work an 80-hour week too.
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Campus Chaos as Pokemon Go Goes Viral

Credit: Burdie

Credit: Burdie

Campuses across the country are facing chaos today as the viral video game Pokemon Go continues to grip the student body.

Dr. Samuel Oak, a professor of zoology at Celadon University, took the drastic step of failing all of his students after they refused to pay attention in class following the release of the hit new game.

“Around the second week of class I noticed many students had stopped paying attention completely and were just staring at their phones”, he lame, “Every class has some inattentive students, but when they began walking around the room I started to get irritated”.

“One day a student pointed their phone at me and exclaimed that they had caught a Butterfree”, he explains, “I just lost it”.

Professor Elm, a biology professor at Johto University said that she had an influx of students interrupting her class on Monday asking if they were in the right place for the Magikarp giveaway.

Elsewhere campus gyms have been designated as a Pokemon gyms, resulting in a number of unfortunate accidents, while the library at Straiton City University is receiving visitors in unprecedented numbers after first year student Ash Ketchum claimed to have spotted a Pikachu in the aisles.

campus

Campuses nationwide have been affected.

Pokemon Go is the latest in a string of distractions that are leaving lecturers helpless – just last year a student was marked absent after spending her class taking selfies and googling pictures of golden retriever puppies in party hats.

But Oak and others argue that this is an entirely new breed of distraction, more involved and insidious than the selfies and emojis that have previously plagued their pedagogy.

Unsure how to manage the crisis, one university is cancelling the semester altogether to allow the hype to die down.

Professor Takao Cozmo, Dean of Fallarbor University, announced the closure today in a brief statement: “We recognize that attempting to teach in this environment is pointless, so all classes are cancelled until further notice” he told the small group of journalists that were all staring at their phones.

Cosmo also announced that research efforts would be redirected to capturing and identifying all 150 of the curious creatures before cutting his speech short and rushing to the door to chase a passing Pidgey.

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5 Out of this World Star Wars Papers

it's a trap

1. It’s a Trap: Emperor Palpatine’s Poison Pill external-link

Abstract: “In this paper we study the financial repercussions of the destruction of two fully armed and
operational moon-sized battle stations (“Death Stars”) in a 4-year period and the dissolution of
the galactic government in Star Wars.” 

Highlights: The whole thing is excellent. Estimating a “$193 QUINTILLION cost for the Death Star (including R&D)”. Concluding that “the Rebel Alliance would need to prepare a bailout of at least 15%, and likely at least 20%, of GGP1 in order to mitigate the systemic risks and the sudden and catastrophic economic collapse”.

galactic bailout

Distribution of the losses caused by the destruction of the second Death Star.

2. Using Star Wars’ supporting characters to teach about psychopathology external-link

Abstract: “The pop culture phenomenon of Star Wars has been underutilised as a vehicle to teach about psychiatry… The purpose of this article is to illustrate psychopathology and psychiatric themes demonstrated by supporting characters, and ways they can be used to teach concepts in a hypothetical yet memorable way… Characters can be used to approach teaching about ADHD, anxiety, kleptomania and paedophilia.”

Highlights: Stating that Jar Jar Binks is the “low-hanging fruit of psychopathology, serving as an easily identifiable example of attention deficit hyperactivity disorder (ADHD)”. Overanalysis of Luke’s familial relations.

star wars table

3. Evolving Ideals of Male Body Image as Seen Through Action Toys external-link

Abstract: “We hypothesized that the physiques of male action toys…  would provide some index of evolving American cultural ideals of male body image… We obtained examples of the most popular American action toys manufactured over the last 30 years. We then measured the waist, chest, and bicep circumference of each figure and scaled these measurements using classical allometry to the height of an actual man (1.78 m)… We found that the figures have grown much more muscular over time…”

Highlights: The accompanying image showing how buff Luke and Anakin became between 1978-1998. “Luke and Hans have both acquired the physiques of bodybuilders over the last 20 years, with particularly impressive gains in the shoulder and chest areas”

Luke & Hans

4. Darth Vader Made Me Do It! Anakin Skywalker’s Avoidance of Responsibility and the Gray Areas of Hegemonic Masculinity in the Star Wars Universe external-link

Abstract: “In this essay, we examined the interactions of Anakin Skywalker during moral dilemmas in the Star Wars narrative in order to demonstrate the avoidance of responsibility as a characteristic of hegemonic masculinity. Past research on sexual harassment has demonstrated a ‘‘gray area’’ that shields sexual harassers from responsibility. We explored how such a gray area functions as a characteristic of hegemonic masculinity by shielding one male, Anakin Skywalker, from responsibility for his immoral and often violent actions. Through our investigation, we found three themes integral for the construction of a gray area that helped Anakin avoid responsibility: phantom altruism, a clone-like will, and the guise of the Sith.”

Highlights: “Other characters within contemporary popular culture—such as Rambo and Jason Bourne—all avoid responsibility for any crimes or violent actions they take when confronted by moral dilemmas within their respective narrative because they all demonstrate themes similar to the three that arose in our analysis of Anakin Skywalker: (a) an altruistic past, (b) threats and deceptions that rob them of their autonomy, and (c) a dark guise that can be blamed for their most egregious actions”

5. The Skywalker Twins Drift Apart external-link

Abstract: “The twin paradox states that twins travelling relativistically appear to age differently to one
another due to time dilation. In the 1980 film Star Wars Episode V: The Empire Strikes Back, twins
Luke and Leia Skywalker travel very large distances at “lightspeed.” This paper uses two scenarios to
attempt to explore the theoretical effects of the twin paradox on the two protagonists.”

HighlightsCapture d’écran 2015-12-18 à 14.46.34 Luke is calculated to be 638.2 days younger than Leia.

  1. Gross Galactic Product
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Academics with Cats Awards 2015 – WINNERS!

1. Best in Show - KirstyLiddiard1

AWCA Winner - @KirstyLiddiard1

 

2. Academics - MikeLNewell

MikeLNewell

 

12. Photography - Paul_Sagar

Paul_Sagar

 

4. Research - andydlbm

andydlbm

 

6. Writing - KirstyLiddiard1

 

8. Teaching - aggguilfordchem

aggguilfordchem

 

10. Assistant - MsHarrietGray

MsHarrietGray

 

3. Academics - dieterhochuli

dieterhochuli

 

5. Research - KatieLBridger

KatieLBridger

 

7. Writing - RuthMostern

RuthMostern

 

 

9. Teaching - andydlbm

andydlbm

 

11. Assistant - TudorWench

TudorWench

 

 

Many thanks to all the other entries that were shortlisted:

ColetteInTheLab

NevilleMorley

KirstyLiddiard1

MercedesRosello

TheShrewUntamed

dannifromdublin

laderafrutal

MarieLouiseLu

PhDgirlSA

AMLTaylor66

ColditzJB

EmodConsumption

SciTania

aimee_e27

BodenZoe

Dannifromdublin

ThrallofYoki

 

And once again a huge thanks to the fantastic expert panel that shortlisted the entries this year:

Chris BrookeChris Brooke
@chrisbrooke
Chris is a Lecturer at Cambridge and co-winner in the first Academics with Cats Awards.

Deborah Fisher
@DrDeborahFisher
Deborah is an Associate Professor of Medicine at Duke University and co-winner of the first Academics with Cats Awards.
Nadine MullerNadine Muller
@Nadine_Muller
Nadine is a Senior Lecturer in English Literature, a BBC Radio 3 New Generation Thinker, and an academic with both cats and dogs.
Cristina RiguttoCristina Rigutto
@cristinarigutto
Cristina is an avid golfer, Sci Comm expert, and tweeter. Her cat tweets @academichashcat.

Camera 360Glen Wright
@AcademiaObscura
Glen is the founder of Academia Obscura. A catless academic, he started #AcademicsWithCats to fill the void.
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The Second Annual Academics with Cats Awards!

You asked for it, and here it is! The Second Academics with Cats Awards launches today!

cat logo

How to enter

Simple! Check out the categories below and tweet your finest cat pics (with caption) to #AcademicsWithCats. We’ll collate them and our expert panel will shortlist the best. Public voting will open on 25 November 2015.

CATegories

This year there are 5 categories. Get creative!

  • Academics and their Cats: you and your feline friend
  • Writing
  • Outreach
  • Impact
  • Teaching

Prizes

Best in Show
Your cat will receive a professorship certificate, mortar board, and collar tag, and will become the Mice Chancellor of Academia Obscura (@MiceChancellor). Your cat will also be entered into the Academic Cats Hall of Fame. You will receive a signed copy of the forthcoming Academia Obscura book.

Category Winners
Your cat will feature in a series of demotivational academic posters (if they so wish!). You will receive a signed copy of the forthcoming Academia Obscura book.

Runners-up
You will receive a free ebook version of the forthcoming Academia Obscura book.

Dates

  • Tuesday 3 November: Launch!
  • Friday 20 November: Entries close
  • 20-25 November: Shortlisting
  • 25 November: Voting opens
  • 15 December: Voting closes
  • 16-18 December: Winner announcements

The shortlisting panel

The shortlist will be diligently put together by the following panel of experts.

Chris BrookeChris Brooke
@chrisbrooke
Chris is a Lecturer at Cambridge and co-winner in the first Academics with Cats Awards.

Deborah Fisher
@DrDeborahFisher
Deborah is an Associate Professor of Medicine at Duke University and co-winner of the first Academics with Cats Awards.
Nadine MullerNadine Muller
@Nadine_Muller
Nadine is a Senior Lecturer in English Literature, a BBC Radio 3 New Generation Thinker, and an academic with both cats and dogs.
Cristina RiguttoCristina Rigutto
@cristinarigutto
Cristina is an avid golfer, Sci Comm expert, and tweeter. Her cat tweets @academichashcat.

Camera 360Glen Wright
@AcademiaObscura
Glen is the founder of Academia Obscura. A catless academic, he started #AcademicsWithCats to fill the void.
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Still Not Significant.

This post originally appeared on Matthew Hankin’s blog, Probable Error. Follow Matthew on twitter @mc_hankins.

What to do if your p-value is just over the arbitrary threshold for ‘significance’ of p=0.05?

You don’t need to play the significance testing game – there are better methods, like quoting the effect size with a confidence interval – but if you do, the rules are simple: the result is either significant or it isn’t.

p_values

An explanation of p-values, by the excellent XKCD comics.

So if your p-value remains stubbornly higher than 0.05, you should call it ‘non-significant’ and write it up as such. The problem for many authors is that this just isn’t the answer they were looking for: publishing so-called ‘negative results’ is harder than ‘positive results’.

The solution is to apply the time-honoured tactic of circumlocution to disguise the non-significant result as something more interesting. The following list is culled from peer-reviewed journal articles in which (a) the authors set themselves the threshold of 0.05 for significance, (b) failed to achieve that threshold value for p and (c) described it in such a way as to make it seem more interesting.

As well as being statistically flawed (results are either significant or not and can’t be qualified), the wording is linguistically interesting, often describing an aspect of the result that just doesn’t exist. For example, “a trend towards significance” expresses non-significance as some sort of motion towards significance, which it isn’t: there is no ‘trend’, in any direction, and nowhere for the trend to be ‘towards’.

Some further analysis will follow, but for now here is the list in full (UPDATE: now in alpha-order):

  • (barely) not statistically significant (p=0.052)
  • a barely detectable statistically significant difference (p=0.073)
  • a borderline significant trend (p=0.09)
  • a certain trend toward significance (p=0.08)
  • a clear tendency to significance (p=0.052)
  • a clear trend (p<0.09)
  • a clear, strong trend (p=0.09)
  • a considerable trend toward significance (p=0.069)
  • a decreasing trend (p=0.09)
  • a definite trend (p=0.08)
  • a distinct trend toward significance (p=0.07)
  • a favorable trend (p=0.09)
  • a favourable statistical trend (p=0.09)
  • a little significant (p<0.1)
  • a margin at the edge of significance (p=0.0608)
  • a marginal trend (p=0.09)
  • a marginal trend toward significance (p=0.052)
  • a marked trend (p=0.07)
  • a mild trend (p<0.09)
  • a moderate trend toward significance (p=0.068)
  • a near-significant trend (p=0.07)
  • a negative trend (p=0.09)
  • a nonsignificant trend (p<0.1)
  • a nonsignificant trend toward significance (p=0.1)
  • a notable trend (p<0.1)
  • a numerical increasing trend (p=0.09)
  • a numerical trend (p=0.09)
  • a positive trend (p=0.09)
  • a possible trend (p=0.09)
  • a possible trend toward significance (p=0.052)
  • a pronounced trend (p=0.09)
  • a reliable trend (p=0.058)
  • a robust trend toward significance (p=0.0503)
  • a significant trend (p=0.09)
  • a slight slide towards significance (p<0.20)
  • a slight tendency toward significance(p<0.08)
  • a slight trend (p<0.09)
  • a slight trend toward significance (p=0.098)
  • a slightly increasing trend (p=0.09)
  • a small trend (p=0.09)
  • a statistical trend (p=0.09)
  • a statistical trend toward significance (p=0.09)
  • a strong tendency towards statistical significance (p=0.051)
  • a strong trend (p=0.077)
  • a strong trend toward significance (p=0.08)
  • a substantial trend toward significance (p=0.068)
  • a suggestive trend (p=0.06)
  • a trend close to significance (p=0.08)
  • a trend significance level (p=0.08)
  • a trend that approached significance (p<0.06)
  • a very slight trend toward significance (p=0.20)
  • a weak trend (p=0.09)
  • a weak trend toward significance (p=0.12)
  • a worrying trend (p=0.07)
  • all but significant (p=0.055)
  • almost achieved significance (p=0-065)
  • almost approached significance (p=0.065)
  • almost attained significance (p<0.06)
  • almost became significant (p=0.06)
  • almost but not quite significant (p=0.06)
  • almost clinically significant (p<0.10)
  • almost insignificant (p>0.065)
  • almost marginally significant (p>0.05)
  • almost non-significant (p=0.083)
  • almost reached statistical significance (p=0.06)
  • almost significant (p=0.06)
  • almost significant tendency (p=0.06)
  • almost statistically significant (p=0.06)
  • an adverse trend (p=0.10)
  • an apparent trend (p=0.286)
  • an associative trend (p=0.09)
  • an elevated trend (p<0.05)
  • an encouraging trend (p<0.1)
  • an established trend (p<0.10)
  • an evident trend (p=0.13)
  • an expected trend (p=0.08)
  • an important trend (p=0.066)
  • an increasing trend (p<0.09)
  • an interesting trend (p=0.1)
  • an inverse trend toward significance (p=0.06)
  • an observed trend (p=0.06)
  • an obvious trend (p=0.06)
  • an overall trend (p=0.2)
  • an unexpected trend (p=0.09)
  • an unexplained trend (p=0.09)
  • an unfavorable trend (p<0.10)
  • appeared to be marginally significant (p<0.10)
  • approached acceptable levels of statistical significance (p=0.054)
  • approached but did not quite achieve significance (p>0.05)
  • approached but fell short of significance (p=0.07)
  • approached conventional levels of significance (p<0.10)
  • approached near significance (p=0.06)
  • approached our criterion of significance (p>0.08)
  • approached significant (p=0.11)
  • approached the borderline of significance (p=0.07)
  • approached the level of significance (p=0.09)
  • approached trend levels of significance (p0.05)
  • approached, but did reach, significance (p=0.065)
  • approaches but fails to achieve a customary level of statistical significance (p=0.154)
  • approaches statistical significance (p>0.06)
  • approaching a level of significance (p=0.089)
  • approaching an acceptable significance level (p=0.056)
  • approaching borderline significance (p=0.08)
  • approaching borderline statistical significance (p=0.07)
  • approaching but not reaching significance (p=0.53)
  • approaching clinical significance (p=0.07)
  • approaching close to significance (p<0.1)
  • approaching conventional significance levels (p=0.06)
  • approaching conventional statistical significance (p=0.06)
  • approaching formal significance (p=0.1052)
  • approaching independent prognostic significance (p=0.08)
  • approaching marginal levels of significance p<0.107)
  • approaching marginal significance (p=0.064)
  • approaching more closely significance (p=0.06)
  • approaching our preset significance level (p=0.076)
  • approaching prognostic significance (p=0.052)
  • approaching significance (p=0.09)
  • approaching the traditional significance level (p=0.06)
  • approaching to statistical significance (p=0.075)
  • approaching, although not reaching, significance (p=0.08)
  • approaching, but not reaching, significance (p<0.09)
  • approximately significant (p=0.053)
  • approximating significance (p=0.09)
  • arguably significant (p=0.07)
  • as good as significant (p=0.0502)
  • at the brink of significance (p=0.06)
  • at the cusp of significance (p=0.06)
  • at the edge of significance (p=0.055)
  • at the limit of significance (p=0.054)
  • at the limits of significance (p=0.053)
  • at the margin of significance (p=0.056)
  • at the margin of statistical significance (p<0.07)
  • at the verge of significance (p=0.058)
  • at the very edge of significance (p=0.053)
  • barely below the level of significance (p=0.06)
  • barely escaped statistical significance (p=0.07)
  • barely escapes being statistically significant at the 5% risk level (0.1>p>0.05)
  • barely failed to attain statistical significance (p=0.067)
  • barely fails to attain statistical significance at conventional levels (p<0.10
  • barely insignificant (p=0.075)
  • barely missed statistical significance (p=0.051)
  • barely missed the commonly acceptable significance level (p<0.053)
  • barely outside the range of significance (p=0.06)
  • barely significant (p=0.07)
  • below (but verging on) the statistical significant level (p>0.05)
  • better trends of improvement (p=0.056)
  • bordered on a statistically significant value (p=0.06)
  • bordered on being significant (p>0.07)
  • bordered on being statistically significant (p=0.0502)
  • bordered on but was not less than the accepted level of significance (p>0.05)
  • bordered on significant (p=0.09)
  • borderline conventional significance (p=0.051)
  • borderline level of statistical significance (p=0.053)
  • borderline significant (p=0.09)
  • borderline significant trends (p=0.099)
  • close to a marginally significant level (p=0.06)
  • close to being significant (p=0.06)
  • close to being statistically significant (p=0.055)
  • close to borderline significance (p=0.072)
  • close to the boundary of significance (p=0.06)
  • close to the level of significance (p=0.07)
  • close to the limit of significance (p=0.17)
  • close to the margin of significance (p=0.055)
  • close to the margin of statistical significance (p=0.075)
  • closely approaches the brink of significance (p=0.07)
  • closely approaches the statistical significance (p=0.0669)
  • closely approximating significance (p>0.05)
  • closely not significant (p=0.06)
  • closely significant (p=0.058)
  • close-to-significant (p=0.09)
  • did not achieve conventional threshold levels of statistical significance (p=0.08)
  • did not exceed the conventional level of statistical significance (p<0.08)
  • did not quite achieve acceptable levels of statistical significance (p=0.054)
  • did not quite achieve significance (p=0.076)
  • did not quite achieve the conventional levels of significance (p=0.052)
  • did not quite achieve the threshold for statistical significance (p=0.08)
  • did not quite attain conventional levels of significance (p=0.07)
  • did not quite reach a statistically significant level (p=0.108)
  • did not quite reach conventional levels of statistical significance (p=0.079)
  • did not quite reach statistical significance (p=0.063)
  • did not reach the traditional level of significance (p=0.10)
  • did not reach the usually accepted level of clinical significance (p=0.07)
  • difference was apparent (p=0.07)
  • direction heading towards significance (p=0.10)
  • does not appear to be sufficiently significant (p>0.05)
  • does not narrowly reach statistical significance (p=0.06)
  • does not reach the conventional significance level (p=0.098)
  • effectively significant (p=0.051)
  • equivocal significance (p=0.06)
  • essentially significant (p=0.10)
  • extremely close to significance (p=0.07)
  • failed to reach significance on this occasion (p=0.09)
  • failed to reach statistical significance (p=0.06)
  • fairly close to significance (p=0.065)
  • fairly significant (p=0.09)
  • falls just short of standard levels of statistical significance (p=0.06)
  • fell (just) short of significance (p=0.08)
  • fell barely short of significance (p=0.08)
  • fell just short of significance (p=0.07)
  • fell just short of statistical significance (p=0.12)
  • fell just short of the traditional definition of statistical significance (p=0.051)
  • fell marginally short of significance (p=0.07)
  • fell narrowly short of significance (p=0.0623)
  • fell only marginally short of significance (p=0.0879)
  • fell only short of significance (p=0.06)
  • fell short of significance (p=0.07)
  • fell slightly short of significance (p>0.0167)
  • fell somewhat short of significance (p=0.138)
  • felt short of significance (p=0.07)
  • flirting with conventional levels of significance (p>0.1)
  • heading towards significance (p=0.086)
  • highly significant (p=0.09)
  • hint of significance (p>0.05)
  • hovered around significance (p = 0.061)
  • hovered at nearly a significant level (p=0.058)
  • hovering closer to statistical significance (p=0.076)
  • hovers on the brink of significance (p=0.055)
  • in the edge of significance (p=0.059)
  • in the verge of significance (p=0.06)
  • inconclusively significant (p=0.070)
  • indeterminate significance (p=0.08)
  • indicative significance (p=0.08)
  • is just outside the conventional levels of significance
  • just about significant (p=0.051)
  • just above the arbitrary level of significance (p=0.07)
  • just above the margin of significance (p=0.053)
  • just at the conventional level of significance (p=0.05001)
  • just barely below the level of significance (p=0.06)
  • just barely failed to reach significance (p<0.06)
  • just barely insignificant (p=0.11)
  • just barely statistically significant (p=0.054)
  • just beyond significance (p=0.06)
  • just borderline significant (p=0.058)
  • just escaped significance (p=0.07)
  • just failed significance (p=0.057)
  • just failed to be significant (p=0.072)
  • just failed to reach statistical significance (p=0.06)
  • just failing to reach statistical significance (p=0.06)
  • just fails to reach conventional levels of statistical significance (p=0.07)
  • just lacked significance (p=0.053)
  • just marginally significant (p=0.0562)
  • just missed being statistically significant (p=0.06)
  • just missing significance (p=0.07)
  • just on the verge of significance (p=0.06)
  • just outside accepted levels of significance (p=0.06)
  • just outside levels of significance (p<0.08)
  • just outside the bounds of significance (p=0.06)
  • just outside the conventional levels of significance (p=0.1076)
  • just outside the level of significance (p=0.0683)
  • just outside the limits of significance (p=0.06)
  • just outside the traditional bounds of significance (p=0.06)
  • just over the limits of statistical significance (p=0.06)
  • just short of significance (p=0.07)
  • just shy of significance (p=0.053)
  • just skirting the boundary of significance (p=0.052)
  • just tendentially significant (p=0.056)
  • just tottering on the brink of significance at the 0.05 level
  • just very slightly missed the significance level (p=0.086)
  • leaning towards significance (p=0.15)
  • leaning towards statistical significance (p=0.06)
  • likely to be significant (p=0.054)
  • loosely significant (p=0.10)
  • marginal significance (p=0.07)
  • marginally and negatively significant (p=0.08)
  • marginally insignificant (p=0.08)
  • marginally nonsignificant (p=0.096)
  • marginally outside the level of significance
  • marginally significant (p>=0.1)
  • marginally significant tendency (p=0.08)
  • marginally statistically significant (p=0.08)
  • may not be significant (p=0.06)
  • medium level of significance (p=0.051)
  • mildly significant (p=0.07)
  • missed narrowly statistical significance (p=0.054)
  • moderately significant (p>0.11)
  • modestly significant (p=0.09)
  • narrowly avoided significance (p=0.052)
  • narrowly eluded statistical significance (p=0.0789)
  • narrowly escaped significance (p=0.08)
  • narrowly evaded statistical significance (p>0.05)
  • narrowly failed significance (p=0.054)
  • narrowly missed achieving significance (p=0.055)
  • narrowly missed overall significance (p=0.06)
  • narrowly missed significance (p=0.051)
  • narrowly missed standard significance levels (p<0.07)
  • narrowly missed the significance level (p=0.07)
  • narrowly missing conventional significance (p=0.054)
  • near limit significance (p=0.073)
  • near miss of statistical significance (p>0.1)
  • near nominal significance (p=0.064)
  • near significance (p=0.07)
  • near to statistical significance (p=0.056)
  • near/possible significance(p=0.0661)
  • near-borderline significance (p=0.10)
  • near-certain significance (p=0.07)
  • nearing significance (p<0.051)
  • nearly acceptable level of significance (p=0.06)
  • nearly approaches statistical significance (p=0.079)
  • nearly borderline significance (p=0.052)
  • nearly negatively significant (p<0.1)
  • nearly positively significant (p=0.063)
  • nearly reached a significant level (p=0.07)
  • nearly reaching the level of significance (p<0.06)
  • nearly significant (p=0.06)
  • nearly significant tendency (p=0.06)
  • nearly, but not quite significant (p>0.06)
  • near-marginal significance (p=0.18)
  • near-significant (p=0.09)
  • near-to-significance (p=0.093)
  • near-trend significance (p=0.11)
  • nominally significant (p=0.08)
  • non-insignificant result (p=0.500)
  • non-significant in the statistical sense (p>0.05
  • not absolutely significant but very probably so (p>0.05)
  • not as significant (p=0.06)
  • not clearly significant (p=0.08)
  • not completely significant (p=0.07)
  • not completely statistically significant (p=0.0811)
  • not conventionally significant (p=0.089), but..
  • not currently significant (p=0.06)
  • not decisively significant (p=0.106)
  • not entirely significant (p=0.10)
  • not especially significant (p>0.05)
  • not exactly significant (p=0.052)
  • not extremely significant (p<0.06)
  • not formally significant (p=0.06)
  • not fully significant (p=0.085)
  • not globally significant (p=0.11)
  • not highly significant (p=0.089)
  • not insignificant (p=0.056)
  • not markedly significant (p=0.06)
  • not moderately significant (P>0.20)
  • not non-significant (p>0.1)
  • not numerically significant (p>0.05)
  • not obviously significant (p>0.3)
  • not overly significant (p>0.08)
  • not quite borderline significance (p>=0.089)
  • not quite reach the level of significance (p=0.07)
  • not quite significant (p=0.118)
  • not quite within the conventional bounds of statistical significance (p=0.12)
  • not reliably significant (p=0.091)
  • not remarkably significant (p=0.236)
  • not significant by common standards (p=0.099)
  • not significant by conventional standards (p=0.10)
  • not significant by traditional standards (p<0.1)
  • not significant in the formal statistical sense (p=0.08)
  • not significant in the narrow sense of the word (p=0.29)
  • not significant in the normally accepted statistical sense (p=0.064)
  • not significantly significant but..clinically meaningful (p=0.072)
  • not statistically quite significant (p<0.06)
  • not strictly significant (p=0.06)
  • not strictly speaking significant (p=0.057)
  • not technically significant (p=0.06)
  • not that significant (p=0.08)
  • not to an extent that was fully statistically significant (p=0.06)
  • not too distant from statistical significance at the 10% level
  • not too far from significant at the 10% level
  • not totally significant (p=0.09)
  • not unequivocally significant (p=0.055)
  • not very definitely significant (p=0.08)
  • not very definitely significant from the statistical point of view (p=0.08)
  • not very far from significance (p<0.092)
  • not very significant (p=0.1)
  • not very statistically significant (p=0.10)
  • not wholly significant (p>0.1)
  • not yet significant (p=0.09)
  • not strongly significant (p=0.08)
  • noticeably significant (p=0.055)
  • on the border of significance (p=0.063)
  • on the borderline of significance (p=0.0699)
  • on the borderlines of significance (p=0.08)
  • on the boundaries of significance (p=0.056)
  • on the boundary of significance (p=0.055)
  • on the brink of significance (p=0.052)
  • on the cusp of conventional statistical significance (p=0.054)
  • on the cusp of significance (p=0.058)
  • on the edge of significance (p>0.08)
  • on the limit to significant (p=0.06)
  • on the margin of significance (p=0.051)
  • on the threshold of significance (p=0.059)
  • on the verge of significance (p=0.053)
  • on the very borderline of significance (0.05<p<0.06)
  • on the very fringes of significance (p=0.099)
  • on the very limits of significance (0.1>p>0.05)
  • only a little short of significance (p>0.05)
  • only just failed to meet statistical significance (p=0.051)
  • only just insignificant (p>0.10)
  • only just missed significance at the 5% level
  • only marginally fails to be significant at the 95% level (p=0.06)
  • only marginally nearly insignificant (p=0.059)
  • only marginally significant (p=0.9)
  • only slightly less than significant (p=0.08)
  • only slightly missed the conventional threshold of significance (p=0.062)
  • only slightly missed the level of significance (p=0.058)
  • only slightly missed the significance level (p=0·0556)
  • only slightly non-significant (p=0.0738)
  • only slightly significant (p=0.08)
  • partial significance (p>0.09)
  • partially significant (p=0.08)
  • partly significant (p=0.08)
  • perceivable statistical significance (p=0.0501)
  • possible significance (p<0.098)
  • possibly marginally significant (p=0.116)
  • possibly significant (0.05<p>0.10)
  • possibly statistically significant (p=0.10)
  • potentially significant (p>0.1)
  • practically significant (p=0.06)
  • probably not experimentally significant (p=0.2)
  • probably not significant (p>0.25)
  • probably not statistically significant (p=0.14)
  • probably significant (p=0.06)
  • provisionally significant (p=0.073)
  • quasi-significant (p=0.09)
  • questionably significant (p=0.13)
  • quite close to significance at the 10% level (p=0.104)
  • quite significant (p=0.07)
  • rather marginal significance (p>0.10)
  • reached borderline significance (p=0.0509)
  • reached near significance (p=0.07)
  • reasonably significant (p=0.07)
  • remarkably close to significance (p=0.05009)
  • resides on the edge of significance (p=0.10)
  • roughly significant (p>0.1)
  • scarcely significant (0.05<p>0.1)
  • significant at the .07 level
  • significant tendency (p=0.09)
  • significant to some degree (0<p>1)
  • significant, or close to significant effects (p=0.08, p=0.05)
  • significantly better overall (p=0.051)
  • significantly significant (p=0.065)
  • similar but not nonsignificant trends (p>0.05)
  • slight evidence of significance (0.1>p>0.05)
  • slight non-significance (p=0.06)
  • slight significance (p=0.128)
  • slight tendency toward significance (p=0.086)
  • slightly above the level of significance (p=0.06)
  • slightly below the level of significance (p=0.068)
  • slightly exceeded significance level (p=0.06)
  • slightly failed to reach statistical significance (p=0.061)
  • slightly insignificant (p=0.07)
  • slightly less than needed for significance (p=0.08)
  • slightly marginally significant (p=0.06)
  • slightly missed being of statistical significance (p=0.08)
  • slightly missed statistical significance (p=0.059)
  • slightly missed the conventional level of significance (p=0.061)
  • slightly missed the level of statistical significance (p<0.10)
  • slightly missed the margin of significance (p=0.051)
  • slightly not significant (p=0.06)
  • slightly outside conventional statistical significance (p=0.051)
  • slightly outside the margins of significance (p=0.08)
  • slightly outside the range of significance (p=0.09)
  • slightly outside the significance level (p=0.077)
  • slightly outside the statistical significance level (p=0.053)
  • slightly significant (p=0.09)
  • somewhat marginally significant (p>0.055)
  • somewhat short of significance (p=0.07)
  • somewhat significant (p=0.23)
  • somewhat statistically significant (p=0.092)
  • strong trend toward significance (p=0.08)
  • sufficiently close to significance (p=0.07)
  • suggestive but not quite significant (p=0.061)
  • suggestive of a significant trend (p=0.08)
  • suggestive of statistical significance (p=0.06)
  • suggestively significant (p=0.064)
  • tailed to insignificance (p=0.1)
  • tantalisingly close to significance (p=0.104)
  • technically not significant (p=0.06)
  • teetering on the brink of significance (p=0.06)
  • tend to significant (p>0.1)
  • tended to approach significance (p=0.09)
  • tended to be significant (p=0.06)
  • tended toward significance (p=0.13)
  • tendency toward significance (p approaching 0.1)
  • tendency toward statistical significance (p=0.07)
  • tends to approach significance (p=0.12)
  • tentatively significant (p=0.107)
  • too far from significance (p=0.12)
  • trend bordering on statistical significance (p=0.066)
  • trend in a significant direction (p=0.09)
  • trend in the direction of significance (p=0.089)
  • trend significance level (p=0.06)
  • trend toward (p>0.07)
  • trending towards significance (p>0.15)
  • trending towards significant (p=0.099)
  • uncertain significance (p>0.07)
  • vaguely significant (p>0.2)
  • verged on being significant (p=0.11)
  • verging on significance (p=0.056)
  • verging on the statistically significant (p<0.1)
  • verging-on-significant (p=0.06)
  • very close to approaching significance (p=0.060)
  • very close to significant (p=0.11)
  • very close to the conventional level of significance (p=0.055)
  • very close to the cut-off for significance (p=0.07)
  • very close to the established statistical significance level of p=0.05 (p=0.065)
  • very close to the threshold of significance (p=0.07)
  • very closely approaches the conventional significance level (p=0.055)
  • very closely brushed the limit of statistical significance (p=0.051)
  • very narrowly missed significance (p<0.06)
  • very nearly significant (p=0.0656)
  • very slightly non-significant (p=0.10)
  • very slightly significant (p<0.1)
  • virtually significant (p=0.059)
  • weak significance (p>0.10)
  • weakened..significance (p=0.06)
  • weakly non-significant (p=0.07)
  • weakly significant (p=0.11)
  • weakly statistically significant (p=0.0557)
  • well-nigh significant (p=0.11)
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