6 Phrases that Should be Banned

By Dr George Gosling

Academia, whether that means teaching or studying, is ultimately a matter of communication. Our words are the lifeblood of what we do. So I regularly find myself stuggling to suppress my inner pedant when I read phrases that I know simply don’t do what they’re supposed to. So, if for no other reason than to release the build up of pedantry, here are my top six offenders. Of course, these are things for which I’d never dream of marking down a student, but I might counsel them against. If you use them all the time, it’s nothing personal.

  • It could be argued that…

This is one that gets used endlessly in student essays, and it’s hard to blame them when it’s used so frequently in academic texts. Unfortunately it is absolutely meaningless. Anything could be argued. I could write a blog post putting forward an argument for the sun being The Great Mother Satsuma, but I’d struggle to make the case convincingly. One of the things students find hardest to master is acknowledging complexity while still putting forward a strong argument. For me, this is the wrong side of the line. Arguably, starting a sentence by sitting on the fence like this is a bad habit to get into, as you can easily find yourself opting for this over and over, and miss the fact you haven’t actually argued anything. If you’re not convinced, attribute it to someone who is.

  • On the other hand…

There is a simple way to structure an essay: argument, counter-argument, conclusion. It is easy, but I tend to advise against it. This is often a shock to those students who’ve had it drummed into them at A-Level. Structuring an essay this way is not wrong. It’s actually a straightforward way of producing an acceptable essay. However, it’s a really difficult way of writing really good essay. This is because it creates a number of traps – forcing you to simplify the discussion into two sides when it’s probably much more complex, and making it all too easy to avoid actually having an argument of your own until the closing sentences. No. Start with the argument and then make the case.

  • a biased source

In fact, in my seminars I recommend students ditch the term ‘bias’ altogether. There is no person, no document (no historical witness or source) that is not biased in some way or another. Again, it’s meaningless. The problem here is that labelling a source as biased sounds like you’ve actually said something when you haven’t, making it all too easy to move on to the next point without actually having made one at all. Instead, identify the perspective from which a source is written, or from which they see events. That really can tell us something.

  • some historians

What happened is history (the past). How we interpret, explain and debate the cause, impact and meaning of what happened is History (the scholarly discipline). This wouldn’t be possible if all historians agreed, so there is some sense in distinguishing between the ideas and opinions of some historians and others. The problem is the obvious question it prompts: which ones? Not specifying implies historians are interchangeable, that the positions we take are random. We’re not and they’re not. This is why labelling historians as traditionalist and revisionist likewise falls short – suggesting it’s a fluke of timing. Once again this phrase only does half the job.

  • …but then she is a feminist historian

The objective historian is a myth. Once we recognise we are all biased commentators it can serve as a useful myth – giving license to rigorously question our own assumptions against both the available evidence and the wisdom of the crowd. This is a good thing, yet it’s often cut short by the negative connotations of bias. Labelling the premise of the historian’s assumptions should be a helpful way of engaging with their perspective on the past, but instead is often used to dismiss alternative interpretations rashly. Most typically I see this dismissal – sometimes this bluntly – to reject the arguments of feminist historians. Although I’ve never encountered this said of a male historian.

  • as to

I used to use this all the time about a decade ago, and there’s no zealot like a convert. The reason as to why I turned against this unnecessary flourish is that it’s pretentious. I’ve never used it when speaking, so why when writing? It’s the over-compensating that comes from not feeling you have the authority to write about a given subject. There will always be an element of fake it ’til you make it, but this is too transparent a disguise it be any use. Good academic writing is a matter of saying complicated things as simply as possible. Decide what needs saying. Say it plainly. Then stop.

This post originally appeared on Dr George Gosling’s blog. It is reposted here under the terms of Creative Commons license BY-NC 4.0. Dr Gosling is a Historian of medicine and charity in modern Britain and beyond. Follow him on twitter @gcgosling.

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,

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.

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.

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

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)

A New Academic Year Begins… Bring on the Ig Nobels!

Summer is, sadly, over. Freshers week is, thankfully, also over. And yet another academic year kicked off with that most amusing of academic traditions: the Ig Nobel Prizes.

This year the Ig Nobels, which recognise research that “first makes people laugh then makes them think”, celebrated its 25th first annual award ceremony.

In case you’ve never heard of the Ig Nobels, they are described by singer Amanda Palmer, herself a little off-the-wall, as “a collection of, like, actual Nobel Prize winners giving away prizes to real scientists for doing f’d-up things…”

Ig Nobels Harvard

For a quarter-century, the Igs have been dishing out prizes for unusual research, ranging from the infamous case study of homosexual necrophilia in ducks, to the 2001 patent issued for a “circular transportation facilitation device” (i.e. a wheel).

The award ceremony takes place in Harvard’s largest theatre and resembles something akin to the Oscars crossed with the Rocky Horror Show. The lucky winners, drawn from a field of 9,000 hopefuls, are indeed presented their prize by a one of a “group of genuine, genuinely bemused Nobel Laureates”.

Michael Smith

In 2010, one scientist became the first to win both an Ig and a real Nobel: Sir Andre Geim was awarded the former for his work on graphene, and the latter for levitating a frog with super strong magnets (Geim also co-authored a paper with his pet hamster, Tisha).

By the far the most bizarre this year is a study in which chickens were fitted with prosthetic tails to see if their modified gait could provide clues as to how dinosaurs walked (yes, there is a video).

Sans titre

Other gems this year include:

  • A chemical recipe to partially un-boil an egg.
  • A paper answering the question: “Is ‘Huh’ A Universal World?”
  • A series of studies looking at the biomedical benefits, and consequences, of intense kissing.

If you are looking for a bit of distraction after the whirlwind of the first weeks of term, you can watch the whole ceremony online, or explore all the prizes to date with this neat data viz tool.

As is now the norm, the whole thing was also live-tweeted (#IgNobel). In fact, the Igs employ an “official observer” to linger on stage, head buried in smartphone, for this purpose.

Elsewhere on Twitter this week, I discovered:

  • That animated gifs make for great academic metaphors:

    • That the resident penguin at the University of Portsmouth library has its own account:

  • That National Punctuation Day is a thing: