[Informatics] Authentication Records for Informatics

Fri Apr 15 11:50:13 AEST 2016

Hi Stephen,

Last two pages at:

http://www.vcaa.vic.edu.au/Documents/vce/computing/Informatics_SBA.pdf

Many thanks
Judy Zuccon

On 15 April 2016 at 11:24, Stephen Trouse <
Stephen.Trouse at flinders.vic.edu.au> wrote:

> Hi Everyone,
>
>
>
> I have the authentication record and assessment sheet for Software Dev but
> not for Informatics.  Does anyone know where I can find those?
>
>
>
> Stephen
>
>
>
> *From:* informatics-bounces at edulists.com.au [mailto:
> informatics-bounces at edulists.com.au] *On Behalf Of *Mark
> *Sent:* Thursday, 14 April 2016 2:03 PM
> *To:* Year 12 VCE Informatics Teachers' Mailing List <
> informatics at edulists.com.au>
> *Subject:* [Informatics] Some problems with statistics [LONG]
>
>
>
> Hi all. For those of you looking for case studies of bad statistical use,
> I found an interesting read in this...
>
>
>
> 'STATISTICS DONE WRONG - THE WOEFULLY COMPLETE GUIDE' by Alex Reinhart
>
> no starch press - info at nostarch.com www.nostarch.com
>
> ISBN-10: 1-59327-620-6 ISBN-13: 978-1-59327-620-1
>
>
>
> Some rather long, but thought-provoking excerpts from the book may be
> useful for you and kids evaluating statistical data during hypothesis
> research.
>
>
>
> In brief: there are many problems to be found in published research data.
>
>
>
> ---------------
>
>
>
> The problem of rejecting valid conclusions because of unimportant errors...
>
>
>
> A conclusion supported by poor statistics can still be correct —
> statistical and logical errors do not make a conclusion wrong, but merely
> unsupported.
>
>
>
> The problem of only publishing exciting findings...
>
>
>
> We only ever see a fraction of medical research, for instance, because few
> scientists bother publishing “We Tried This Medicine and It Didn’t Seem to
> Work.” In addition, editors of prestigious journals must maintain their
> reputation for groundbreaking results, and peer reviewers are naturally
> prejudiced against negative results. When presented with papers with
> identical methods and writing, reviewers grade versions with negative
> results more harshly and detect more methodological errors.
>
>
>
> The pharmaceutical industry seems particularly tempted to bias evidence by
> neglecting to publish studies that show their drugs do not work;
>  subsequent reviewers of the literature may be pleased to find that 12
> studies indicate a drug works, without knowing that 8 other unpublished
> studies suggest it does not. Of course, it’s likely that such results would
> not be published by peer-reviewed journals even if they were submitted—a
> strong bias against unexciting results means that studies saying “it didn’t
> work” never appear and other researchers never see them. Missing data and
> publication bias plague science, skewing our perceptions of important
> issues.
>
>
>
> The problem with small sample sizes...
>
>
>
> In the United States, counties with the lowest rates of kidney cancer tend
> to be Midwestern, Southern, and Western rural counties. Why might this be?
> Maybe rural people get more exercise or inhale less-polluted air. Or
> perhaps they just lead less stressful lives.
>
> On the other hand, counties with the highest rates of kidney cancer tend
> to be Midwestern, Southern, and Western rural counties.
>
> The problem, of course, is that rural counties have the smallest
> populations. A single kidney cancer patient in a county with 10 residents
> gives that county the highest kidney cancer rate in the nation. Small
> counties hence have much more variation in kidney cancer rates simply
> because they have so few residents.
>
>
>
> The problem with false positives that sound exciting...
>
>
>
> http://xkcd.com/882
>
>
>
> The problem with Correlation and Causation
>
>
>
> When you have used multiple regression to model some outcome—like the
> probability that a given person will suffer a heart attack, given that
> person's weight, cholesterol, and so on — it’s tempting to interpret each
> variable on its own. You might survey thousands of people, asking whether
> they’ve had a heart attack and then doing a thorough physical examination,
> and produce a model. Then you use this model to give health advice: lose
> some weight, you say, and make sure your cholesterol levels fall within
> this healthy range. Follow these instructions, and your heart attack risk
> will decrease by 30%!
>
> But that's not what your model says. The model says that people with
> cholesterol and weight within that range have a 30% lower risk of heart
> attack; it doesn’t say that if you put an overweight person on a diet and
> exercise routine, that person will be less likely to have a heart attack.
> You didn't collect data on that! You didn't intervene and change the weight
> and cholesterol levels of your volunteers to see what would happen.
>
> There could be a confounding variable here. Perhaps obesity and high
> cholesterol levels are merely symptoms of some other factor that also
> causes heart attacks; exercise and statin pills may fix them but perhaps
> not the heart attacks.
>
> The regression model says lower cholesterol means fewer heart attacks, but
> that's correlation, not causation.
>
> One example of this problem occurred in a 2010 trial testing whether
> omega-3 fatty acids, found in fish oil and commonly sold as a health
> supplement, can reduce the risk of heart attacks. The claim that omega-3
> fatty acids reduce heart attack risk was supported by several observational
> studies, along with some experimental data. Fatty acids have
> anti-inflammatory properties and can reduce the level of triglycerides in
> the bloodstream—two qualities known to correlate with reduced heart attack
> risk. So it was reasoned that omega-3 fatty acids should reduce heart
> attack risk.
>
> But the evidence was observational. Patients with low triglyceride levels
> had fewer heart problems, and fish oils reduce triglyceride levels, so it
> was spuriously concluded that fish oil should protect against heart
> problems. Only in 2013 was a large randomized controlled trial published,
> in which patients were given either fish oil or a placebo (olive oil) and
> monitored for five years. There was no evidence of a beneficial effect of
> fish oil.
>
> Another problem arises when you control for multiple confounding factors.
> It’s common to interpret the results by saying, “If weight increases by one
> pound, with all other variables held constant, then heart attack rates
> increase by...” Perhaps that is true, but it may not be possible to hold
> all other variables constant in practice. You can always quote the numbers
> from the regression equation, but in reality the act of gaining a pound of
> weight also involves other changes. Nobody ever gains a pound with all
> other variables held constant, so your regression equation doesn’t
> translate to reality.
>
>
>
> The problem of Simpson's Paradox
>
>
>
> When statisticians are asked for an interesting paradoxical result in
> statistics, they often turn to Simpson’s paradox. Simpson's paradox arises
> whenever an apparent trend in data, caused by a confounding variable, can
> be eliminated or reversed by splitting the data into natural groups. There
> are many examples of the paradox, so let me start with the most popular.
>
> In 1973, the University of California, Berkeley, received 12,763
> applications for graduate study. In that year’s admissions process, 44% of
> male applicants were accepted but only 35% of female applicants were. The
> university administration, fearing a gender discrimination lawsuit, asked
> several of its faculty to take a closer look at the data.
>
> Graduate admissions, unlike undergraduate admissions, are handled by each
> academic department independently. The initial investigation led to a
> paradoxical conclusion: of 101 separate graduate departments at Berkeley,
> only 4 departments showed a statistically significant bias against
> admitting women. At the same time, six departments showed a bias against
> men, which was more than enough to cancel out the deficit of women caused
> by the other four departments.
>
> How could Berkeley as a whole appear biased against women when individual
> departments were generally not? It turns out that men and women did not
> apply to all departments in equal proportion. For example, nearly
> two-thirds of the applicants to the English department were women, while
> only 2% of mechanical engineering applicants were. Furthermore, some
> graduate departments were more selective than others.
>
> These two factors accounted for the perceived bias. Women tended to apply
> to departments with many qualified applicants and little funding, while men
> applied to departments with fewer applicants and surpluses of research
> grants. The bias was not at Berkeley, where individual departments were
> generally fair, but further back in the educational process, where women
> were being shunted into fields of study with fewer graduate opportunities.
>
>
>
> The problem with making mistakes
>
>
>
> Surveys of statistically significant results reported in medical and
> psychological trials suggest that many p values are wrong and some
> statistically insignificant results are actually significant when computed
> correctly. Even the prestigious journal Nature isn’t perfect, with roughly
> 38% of papers making typos and calculation errors in their p values. Other
> reviews find examples of misclassified data, erroneous duplication of data,
> inclusion of the wrong dataset entirely, and other mix-ups, all concealed
> by papers that did not describe their analysis in enough detail for the
> errors to be easily noticed.
>
>
>
> The problem of data decay when seeking to verify the data used in previous
> research
>
>
>
> Another problem is the difficulty of keeping track of data as computers
> are replaced, technology goes obsolete, scientists move to new
> institutions, and students graduate and leave labs. If the dataset is no
> longer in use by its creators, they have no incentive to maintain a
> carefully organized personal archive of datasets, particularly when data
> has to be reconstructed from floppy disks and filing cabinets. One study of
> 516 articles published between 1991 and 2011 found that the probability of
> data being available decayed over time. For papers more than 20 years old,
> fewer than half of datasets were available.Some authors could not be
> contacted because their email addresses had changed; others replied that
> they probably have the data, but it’s on a floppy disk and they no longer
> have a floppy drive or that the data was on a stolen computer or otherwise
> lost.
>
>
>
>
>
> Regards, Mark
>
>
>
> with thanks to
>
>
>
> 'STATISTICS DONE WRONG - THE WOEFULLY COMPLETE GUIDE' by Alex Reinhart
>
>
>
> --
>
>
>
> Mark Kelly
>
>
>
> mark at vceit.com
>
> http://vceit.com
>
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-- 

Judy Zuccon

RTO Coordinator

AQUINAS COLLEGE

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