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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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:

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12 Things I Learned About Academia From Google Suggestions

1. University is free in Germany, but elsewhere it’s just business. It is also like riding a bike.

uni is

2. Academia is a pointless, broken cult.

academia is

3. There are many misconceptions about us academics. We are often happy, but not always. We are rarely important, and never well paid!

are academics

4. We are many things:

5. We are not the problem…

academics aren't

6. …but we aren’t everything either.

academics are not

7. Economics students are promiscuous and selfish.

economics students

8. History students are just promiscuous.

history students are

9. As for law students, well…

law students

10. A PhD can be problematic, unless it is in dance.

my phd is

11. Don’t expect to much help from your professor…

my professor doesn't

12. …nor from the hot-but-lazy TA.

my teaching assistant is

Many questions remain.

do academics

 

What does Google suggestions say where you are? Tweet your screenshots to @AcademiaObscura with the hashtag #GoogleAcademia.

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9 Highly Useful Tools for Academics

As you have probably figured out by now, this blog is mostly dedicated to the silly side of academia. But just occasionally it can be surprisingly helpful. A number of colleagues have asked me about the various tools I am using to try and keep my academic work (and life in general) in order; so here are the 9 that I couldn’t do without.

Just a quick note: some of these tools require an initial time investment and/or steep learning curve before you see their full potential – don’t try integrating them into your workflow all at once. I recommend starting off with the one or two tools that you think will be most helpful: E.g. if you are about to start a PhD/massive project, get friendly with Scrivener and a citation manager; if you have 8 post-it notes stuck to your screen, start off with Trello. 

1. Mendeley/Zotero/Endnote
If I could go back and give past me one piece of advice at the start of my academic career, it would be this: use a citation manager! I have found Mendeley works best for me, but the particular platform matters much less than the absolute necessity of integrating some sort of citation management software into your workflow. It may seem like a chore at first, but you will be so glad you did it. I find it hard to believe that so many people still write and format citations manually – once you switch to a citation manager, I promise you will never go back.

scrivener-logo2. Scrivener
Scrivener is a word processor that complements the way you think and work when writing. If Word and its ilk are digital extensions of the typewriters of old, Scrivener is the exact opposite, built from the ground up to redefine the way we use computers to write. Snippets of writing are typed up onto virtual note cards, and can be organised into collections. Flicking between paragraphs and sections easy, and chunks of writing are easily reorganised by dragging and dropping. The powerful export function turns these seemingly disjointed cards into a formatted and ready-to-go doc file.

evernote3. Evernote
The Chinese word for computer literally means “electric brain”, and this is exactly how I would describe Evernote. It is my second brain, a giant electronic notebook storing everything from research-related news stories to recipes. You can clip direct from the web, add photos from your phone, or email notes to be saved away for later. It is available online and syncs across all devices, so you can access your stuff wherever you are.

trello logo4. Trello
Trello is like a virtual drag-and-drop cork board, and it is awesome! Before getting my shit together with Trello I would have at least 3 to-do lists on the go at any given time and would waste an inordinate amount of time faffing around with them. My Trello setup: I have a number of separate lists (today, this week, later, waiting, done); on Monday I figure out what needs to be done that week; then each morning I spend 5-10 minutes looking at the ‘this week’ list, dragging items into the ‘today’ list; when a task is done I drag it into the done list (you could just delete it, but I find dropping something into the done list seriously gratifying), or into the waiting list if I am waiting on action from someone else. Gloriously simple, and incredibly effective.

My Trello setup  Colour coding: red=work, blue=PhD, green=blog, orange=admin

My Trello setup
Colour coding: red=work, blue=PhD, green=blog, orange=admin

unrollme5. Unroll.me
If you want to reach the holy grail of inbox zero, Unroll.me is going to help you get there. It “rolls up” all of your regular email subscriptions and newsletters 1 into one daily/weekly email, and sends it to you at a time of your choosing. That means that you don’t get distracted during the day and that your inbox is reserved for priority emails, rather than being crowded with junk. Unroll.me detects new mailing lists and allows you to leave the messages in your inbox, add them to your rollup, or unsubscribe 2 I started using this in March and to date it has saved my inbox from a whopping 2280 emails.

twitter6. Twitter
Oh Twitter, let me count the ways I love thee! I used to be sceptical of the merits of twitter for academics and researchers, but I am now a convert. Not only has Twitter been instrumental for the whole Academia Obscura project, it has also been extremely useful in my professional life. I use it to keep up to date with developments in my field, request articles, and chat with other researchers.

freedom7. Freedom/Anti-Social
While studies show that looking at pictures of kittens increases your productivity, wasting your day on the internet probably doesn’t. Freedom will lock you out of the internet for a designated period of time.3 The only way to get your connection back is to reset your computer, which is enough of a pain in the arse that you probably won’t do it. If you really do need the internet (e.g. for research) you can use Anti-Social instead, which locks you out of distracting sites. At the time of writing, the two apps can be bought as a bundle for $20 (£13).

coach me8. Coach.me
Coach.Me is a motivation app for building good habits. You set up a tick-list of things you want to do on a regular basis, and check them off daily. I used it to get over my lifelong habit of biting my nails, to start brushing my teeth after lunch at work, and, during writing periods, to commit myself to X pomodoros a day. Not particularly sexy, but eminently practical.

9. An external hard drive/Dropbox/Drive/Crashplan
This one needs no explanation. Back up regularly! Invest in a decent external hard drive and make use of the backup software on your computer. But consider this: “just under 80% of all hard drives will survive to their fourth anniversary”. Therefore it is wise to also get on account with an online file storage service and make sure you have your most important files and documents backed up in multiple places.

Are you using these? What has your experience been? Did I miss something? Comment below or tweet @AcademiaObscura.

 

  1. To distinguish this from unsolicited emails, i.e. spam, the tech community coined the term “Bacn” to describe “email you want but not right now”
  2. Downside – it doesn’t actually unsubscribe you, it just funnels the messages away from your inbox, never to be seen again. This could be problematic if you stop using Unroll.me later and find a sudden influx of all this junk you thought you were rid of. Caveat emptor my friends.
  3. Yes, it is a damning indictment of our culture that ‘Freedom’ means turning off the internet so you can get more work done. Don’t hate the player…
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11 Essential Hashtags for Academics

Academic twitterJust over a year ago I began tweeting as @AcademiaObscura, and in that time I have converted from a twitter sceptic to a fervent advocate. Twitter, and other social media tools, can be invaluable for connecting with others in your field, disseminating your work, and keeping up-to-date with the latest research and news. Indeed, once you are past the hump, Twitter becomes useful for all sorts of things. If you are new to Twitter I highly recommend the Thesis Whisperer’s explanation here (scroll down a little to the using twitter section) and LSE’s guide.

Hashtags are a great way to follow specific discussions, and a number have become staples of the academic twittersphere (side note: I use Tweetdeck to follow numerous hashtags simultaneously – intro here). This list is an attempt to introduce the essentials. Special thanks to Raul Pacheco-Vega, whose extremely useful post provided the basis and inspiration for this.

1. #PhDchat
The hashtag for all things PhD, PhDchat is a staple of academic Twitter, having been initially started all the way back in December 2009 by Nasima Riazat (@NSRiazat). A great place to discuss your research progress, get tips and tricks, share experiences etc. Structured sessions are also hosted:

  • UK/Europe: Wednesday nights, 7.30pm-8.30pm GMT (hosted by Nasima herself)
  • Australia: usually the first Wednesday each month, 7pm-8pm Sydney time (hosted by Inger Mewburn – @thesiswhisperer)

More: There is a satisfyingly geeky analysis of the #PhDchat community here.

2. #ECRchat/#AdjunctChat
As above, but specifically for ‘Early Career Researchers’ (ECR) and adjuncts.

3. #AltAc/#PostAc/#WithAPhD
A trio of useful hashtags for those trying to find alternative academic paths, get out of academia altogether, or figure out what to do with a PhD. Jennifer Polk (@FromPhDToLife) is your go-to person on all of these!

600_3663352324. #shutupandwrite
‘Shut Up and Write’, aside from being a great mantra in general, is the name for informal writing groups convened the world over. I guarantee that attending such a group will be the best decision you ever make in terms of writing productivity. But if there isn’t a group near you (and you don’t have the inclination to start one) you can join one virtually through twitter! They take place on the 1st & 3rd Tuesday each month (#suwtues):

5. #AcWri
AcWri, short for ‘academic writing’ is a great place to find helpful tips, motivational tidbits, and articles about the writing process itself.

6. #ICanHazPdf
Have you ever gone to download that crucial paper you need only to find that it is behind a paywall? If your institutional subscriptions don’t cover what you are looking for, simply tweet the details of the paper along with the hashtag and an email address. Usually someone will come through with the paper pretty quickly. Don’t forget to delete your tweet after!

More: Check out some interesting analysis of #ICanHazPdf here and here, and critical discussions here and here.

7. #ScholarSunday
There is a tradition on Twitter of doing #FollowFriday (or #ff) for short – sending a tweet with a few names of people you recommend to others. Raul Pacheco-Vega created Scholar Sunday to go a step further, calling on academics to share not only who they recommend, but also why.

More: discussion from the hashtags creator.

8. #AcaDowntime
Amongst all the writing, teaching, and general stress of academic life, it is more important than ever to set aside for rest and relaxation. #AcaDowntime calls for academics to share what fun things they are up to on their weekends and in their free time. Hopefully we can foster a culture of work-life balance and encourage us all to take time for ourselves.

More: I asked academics what they do in their ‘free’ time. Here’s what they said. Also read “The Workaholic and Academia: in defense of #AcaDowntime

9. Whatever is used in your field
There are many subject-specific hashtags: #twitterstorians, #realtimechem, #TrilobyteTuesday#archaeology#gistribe#runology (for the study of runes, not running)… Poke around a bit and you are bound to find something to take your fancy!

(Just for fun)

10. #AcademicsWithCats Are you an academic? Do you have a cat? Then this hashtag is for you. All the cute cats and kittens you could ever need, often in academic settings.  

More: A day in the life of an academic, with cats; The first annual Academics with Cats Awards.

11. #AcademicsWithBeer If you don’t have a cat but you do love beer, this one’s for you! We have Elena Milani (@biomug) to thank for this recent edition.

More: Read the call to arms (The King’s Arms, that is).

Did I miss anything? What are your favorites? Please post a comment or tweet me @AcademiaObscura. Happy tweeting!

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What PhD Life is Really Like

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.

When you’re studying for a PhD, you will be perpetually presented with two semi-rhetorical questions:

  1. Wow, you must be really smart?
  2. Wow, so you’re gonna be a Doctor!?

Regardless of how tedious these become, you better get used to it because it’s all any non-PhD-student really understands about it. We minions in the lower echelons of academia know it’s a different story altogether.

Whether you’re embarking on a PhD, you know someone studying a PhD and you want to understand their life a little better, or if you’re doing a PhD and just procrastinating today (I’m not here to judge, man) I’ll share what PhD life is really like.

Source: exloringhandhygiene.wordpress.com

Source: exloringhandhygiene.wordpress.com

We’re old, and a student

PhD students are generally older than your average Undergrad or Masters student. We have (considerably) less money than anyone else our age, we shop at Lidl and Aldi, and a night out/celebration are limited to:

  • Student clubs at the weekend – We can have a night out and taxi home for less than £20 but this involves warm syrupy cranberry juice mixed with paint stripper vodka in a plastic cup surrounded by girls wearing shorts/heels/crop tops and boys who resemble our baby brothers; or
  • Fancier pubs during the week – There are fewer crowds, so you can actually chat to your pals, cocktails come in REAL glasses and are often half price during the week. The problem is you can only really go out with your PhD pals because everyone else has to be in the office for 9am.
Source: author's personal collection.

Source: author’s personal collection.

What’s your PhD about anyway?

Let me tell you right now, 90% of people who ask this question aren’t interested in what your PhD is about at all. The other 10% is made up of:

  • Your Supervisors – You take up a lot of their time so naturally they are interested but this interest is VERY low down in their list of priorities;
  • People at a conference who are researching something similar – These people are the tiny percentage of people who actually understand your research and who are genuinely interested. Believe me, this is rare.

So how do you deal with this question from the other 90% of people who don’t care and are asking out of politeness? Well, you reel off a small catchy sums-it-up-sentence people can relate to

For example:

I’m researching why people are scared of attending the dentist.

People love this, and it generates a discussion that most people can join in with. Is it what my research is about? No.

My research evaluates the efficacy of interventions by oral health support workers trying to engage hard to reach families, typically people with a fear of attending the dentist, regarding oral health behaviours. My working title is:

Optimising the role of the Dental Health Support Worker in Childsmile Practice: A qualitative case study approach. 

You see the distinction?

The Doctor thing

Most people who know me, and my journey to get here, get excited about the whole ‘becoming a Doctor’ thing. I appreciate their support but I can’t share the enthusiasm because the shiny appeal of being Dr Mairi Young is well and truly lost.

Let me take you on a journey:

  • 3-6months into a PhD you’re worried about being found out as a fraud to even consider being awarded the doctorate. You’re convinced the University has made a mistake and will call you any day now to kick you out.
  • 1st year you have no idea what to do, so you wing it.
  • 2nd year you worry whether you’ve got enough time to do all your research and writing.
  • 3rd year you panic because you don’t think you’ve done enough to even put together a thesis.
  • By 4th year you’re worrying about PostDocs, Viva’s and the sheer cost of binding the thesis.

By the time your graduation comes around, you’re in the gown and you’re being handed the piece of paper which allows you to call yourself Doctor, you’re already in a Post Doc post and that journey has started all over again.

Forgive us if we aren’t all that excited about being called Doctor. It can feel like something of a consolation prize.

Endless corrections

11704948_10153079810253736_7499609512061010656_nThe one thing I miss about undergrad life was handing in an essay and never seeing it again. You’d receive a mark and that was it: a pure and beautiful cycle of hard work and reward.

This doesn’t happen at PhD level. 

In a PhD you will spend weeks, if not months, tirelessly working on a chapter to make it perfect. You will submit to your supervisors and wait. And wait. And wait. Then you get corrections back.

That beautiful piece of work you worked yourself to the bone for returns covered in incomprehensible scribbles. Deciphering these scribbles will become a skill fit for your CV. Once you’ve deciphered and amended the chapter to perfection, submitted the chapter and waited, and waited and waited…. You get corrections back again.

Thus it continues until the day the thesis is bound and submitted. It’s thoroughly de-motivating and an exhaustive task.

Endnote (or Zotero/Mendeley etc.)

If I could give PhD students one piece of advice for the future, it would be this: learn how to use Endnote.

I’m 2 years and 10 months into a 4 year PhD and I still don’t really know how to work EndNote.

Supposedly it makes your life easier because you can ‘cite while you write’ and compile your references at the click of a button. Yet as I still don’t really know how to work Endnote chances are I (and a couple other PhD’ers I know) will be typing ours out manually.

Please know I’m not lazy, and I’ve not been avoiding the issue. I simply never fully appreciated the time I had on my hands in the first 6 months of my PhD. Back then, I had all the time in the world to spend learning the detailed intricacies of useful software. 3 years in, I don’t have this luxury anymore.

phd 3

Council Tax

Quite possibly the saving grace of the whole PhD malarkey: No Council Tax.

I did my Undergraduate degree, MSc and PhD back to back which means I’ve been studying for 9 years (with a year to go *weeps*). I have not paid council tax at all during this time. I truly believe Glasgow City Council has a ‘Mairi Young Is At It / Must Investigate’ file because after 10 years at 3 different universities, they must be thinking “Surely she’s scamming us?”

Even though I receive a tax free stipend (which FYI is an absolute joy to explain to the Inland Revenue) it is a measly amount, so the saving I make not paying Council Tax means I can afford to live on my own, a luxury I never want to give up.

Undergrads

Contrary to popular opinion, PhD students don’t dislike Undergrads, we’re just jealous of them. Undergrads have it easy and they don’t even know it.

At that age you don’t mind living in a tiny single bedroom and sharing a kitchen with 7 other people so long as it’s within walking distance to class and the student union. You don’t mind living off 9p noodles, cereal and toast. You can sleep during the day between classes, be told exactly what to study to pass the module, submit an essay and never see it again, and you have a whole summer each year in which to relax and enjoy yourself.

PhD students don’t have such luxuries.

We’re too accustomed to the finer things in life: expensive complicated cocktails, antipasti and fresh flowers every weekend. We also read journal papers in bed to catch up with reading, which throws a downer on any romantic relationship, and we stress out over how to afford a suitable outfit for a conference on a measly PhD stipend.

If you’re an undergrad and you see a PhD student tutting at you in the library for browsing Asos rather than working, please know we don’t hate you, we’re just green with envy that our lives are no longer like that. I’m sure you can empathise. By the time you end up doing a PhD, you’ll feel the same, I promise.

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