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:

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.

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.

7 Academic Struggles Predicted by Late 19th and Early 20th Century Autobiographies

avatar_cee947a27aed_64By Alice Violett. Alice is doing a PhD at the University of Essex on the public perceptions and personal experiences of only children in Britain between 1850 and 1950, and blogs at Alice in Academia. Alice likes reading, music, and cats.  You can follow Alice on Twitter @pokesqueak.

My PhD, which is partly about the experiences of only children between 1850 and 1950, has lead to me reading many, many autobiographies. Many are pretty dull (male politicians who skimmed over their childhood years, I’m looking at you!), while others were quite interesting, keeping me reading long after the useful information had been extracted.

Now and again I would come across a funny or odd anecdote that served no useful purpose for my thesis but seemed worth saving. So I did. Upon reflection, I realise I’ve essentially collected an allegory for the academic experience.

monk-big-book1. When the book you need to read is unnecessarily dense

Gay rights campaigner Antony Wright’s uncle, aunt and cousins moved to South Africa in 1933, prompting a wave of creative writing:

I wrote several stories about my cousins’ adventures in the African bush … One vivid phrase I remember (because it caused the grown-ups so much amusement) described how, hearing a loud noise, Bobbie turned and ‘beheld a lion clearing its throat’. Fiction has never been my strong point, and I suspect such purple prose might make even a Barbara Cartland blush. 1

2. When you’ll do anything to get a research assistant

As a child, artist W. Graham Robertson happened upon a spellbook with instructions on ‘how to raise a Fairy’:

“Take the blood of a white hen.” – How could I take the blood of any hen, let alone that the hen would be my uncle’s, and I felt sure he would not care for its blood to be taken? Would the Fairy object to the hen’s being cooked, as, if not, I could save some gravy from dinner?

I gave up on my study of Conjuration, which in most cases seemed to require a little private abattoir of one’s own, for want of proper professional guidance…2

3. When reviewer three questions your knowledge of your own work

Fuller-Maitland liked to play with the boys next door:

One day, as we were all playing in their garden, my nurse hung her head out of a side-window and shouted, “Master John, jest you come in, and don’t let them little Nickles teach you any more bad words!” … To me personally it was galling to be considered as the humble learner in the branch of study indicated. 3

4. When colleagues competitively compare workloads

Music critic John Fuller-Maitland’s aunts moved to Brighton for their health:

One was heard to say, “I hear that Anne calls herself the queen of invalids; and everybody in Brighton knows that I am the queen of invalids”. 4

Your colleague's conference is here. Probably one of these.

Your colleague’s conference is here. Probably one of these.

5. When your colleagues conferences are in more exotic climes than yours

Sociologist and historian Alan Fox, as an impartial outsider, was often called upon to judge which of his better-off friends had it best:

Which did I think was better; ten days on the Belgian coast or three weeks at Dovercourt?5 

[NB: Dovercourt was a popular English seaside resort in the 1920s and 1930s, but is much less glamorous these days!

What even is that?

What even is that?
Credit: a hilarious complaint letter to Virgin, published by the Telegraph.

6. When the conference food options are puzzling

Fuller-Maitland’s parents were worried about his health:

I had to consume a sponge-cake and a glass of port in the middle of the morning for no special reason that I can recall. I have no doubt that this habit sowed the seeds of gout, from which I suffered a good deal at an unusually early age.6

7. When you need to impress an important potential contact

giphyAs a young boy, politician Sir George Leveson Gower was instructed by his uncle how to present a bouquet to the Empress Eugenie at a garden party:

When the Empress came the next day, I got confused, made my bow to my uncle, and as I presented my back to her and was dressed in a short white petticoat, the effect was unconventional.7

Some of the autobiographies I’ve read hadn’t been borrowed from the library in years, and it seemed like a shame to send them back without noting down some of their less relevant content.

Resurrect an old book from the storeroom today!

  1. A E G Wright (Antony Grey), Personal Tapestry, (London, 2008), p. 13.
  2.  W. Graham Robertson, Time Was, (London, 1931), p. 20.
  3.  J. A. Fuller-Maitland, A Door-Keeper of Music, (London, 1929), pp. 16-17.
  4.  J. A. Fuller-Maitland, A Door-keeper of Music, (London, 1929), p. 8.
  5.  Alan Fox, A Very Late Development: An Autobiography, (Coventry, 1990), p. 20.
  6. J. A. Fuller-Maitland, A Door-Keeper of Music, (London, 1929), p. 17.
  7. Sir George Leveson Gower, Years of Content, 1858-1886, (London, 1940), p. 2.

Academics with BEER!

I love beer. So does Elena Milani (@biomug). When the Italian Neuroscientist and SciComm expert realised that no hashtag yet existed for academic beer-lovers, she set about creating one. This is her call to arms! 

tapsThe Internet and social media are plenty of cute fluffy cats, because kittens sell, especially among academics. Everybody knows that!

But what about beer? I love craft beer (and kittens, of course), and in Twitter I’ve found many hashtags on beers such as #beer #craftbeer #beerbods #beertography #breweries #beerselfie and so on.

However, there isn’t a hashtag for academics who love beer, as me, and I was curious if beer could help me to engage others scholars in Twitter. So, I started “an experiment” launching #academicswithbeer with the help of Cristina Rigutto.

A lot of people replied, retweeted and favorited this tweet! And you are invited to join the conversation too!

You can tweet:

  • Quotes
  • Selfies
  • Sketches
  • Sketchnotes or mind maps
  • Other pics or texts

But you must include beer in your pic/text/tweet!

Now, join the #academicswithbeer stream 😉

This post originally appeared at Elena’s blog, SciCommLab.