Archive for survey
computing Bayes, with some approximations
Posted in Books, Statistics, University life with tags Approximate Bayesian computation, Australia, Bayesian computing, IMS, Institute of Mathematical Statistics, Melbourne, Monash University, publishing, Queensland, Surfers Paradise, survey, Victoria, Victoria wines on March 10, 2024 by xi'an
the academic integrity flow chart
Posted in Kids, pictures, University life with tags academic integrity, academic misconduct, dissertation, flowchart, module leader, plagiarism, simulations, survey, University of Warwick on January 24, 2024 by xi'an
This year, I received a summer project dissertation at Warwick (among several I supervised) that was a direct aggregation of three main papers on the project topic, including advanced simulations that were clearly beyond the reach of a summer project. Especially when the perpetrator only attended a very few supervision sessions among those I proposed.
With the help of a colleague we found rather easily the three papers, which had been rewritten to some extent into the project, while keeping the plan of the originals. And then I later a fourth paper corresponding to the numerical illustrative component of the project, which was the original reason for suspecting foul play. With graphs redrawn! Meaning that a plagiarism detector was only achieving an 18% agreement with the available literature, but still flagging plagiarism as “highly likely.”
I thus referred the case to the colleague in charge of academic integrity in the department. 
And this initiated a very involved process summarised by the attached flowchart… Starting with the academic conduct panel, which also concluded at plagiarism.
top off…
Posted in Statistics with tags college ranking, International Statistical Review, John Wiley, PSL, Rao-Blackwellisation, survey, Université Paris Dauphine, University of Warwick on April 25, 2023 by xi'an
Number savvy [book review]
Posted in Books, Statistics with tags #ERCSyG, book review, Brave New World, Brussels, Bruxelles, Canada, Census, CHANCE, CRC Press, data, data market, data pooling, data privacy, demonstrations, ERC Synergy Grant, EU, European Research Council, European Union, Gödel Escher and Bach, GDPR, geography, Kurt Gödel, microdata, Nassim Taleb, Paris, prospect theory, savvy, Statistics Canada, survey, Taoism on March 31, 2023 by xi'an“This book aspires to contribute to overall numeracy through a tour de force presentation of the production, use, and evolution of data.”
Number Savvy: From the Invention of Numbers to the Future of Data
is written by George Sciadas, a statistician working at Statistics Canada. This book is mostly about data, even though it starts with the “compulsory” tour of the invention(s) of numbers and the evolution towards a mostly universal system and the issue of measurements (with a funny if illogical/anti-geographical confusion in “gare du midi in Paris and gare du Nord in Brussels” since Gare du Midi (south) is in Brussels while Gare du Nord (north) in in Paris). The chapter (Chap. 3) on census and demography is quite detailed about the hurdles preventing an exact count of a population, but much less about the methods employed to improve the estimation. (The request for me to fill the short form for the 2023 French Census actually came while I was reading the book!)
The next chapter links measurement with socio-economic notions or models, like unemployment rate, which depends on so many criteria (pp. 77-87) that its measurement sounds impossible or arbitrary. Almost as arbitrary as the reported number of protesters in a French demonstration! Same difficulty with the GDP, whose interpretation seems beyond the grasp of the common reader. And does not cover significantly missing (-not-at-random) data like tax evasion, money laundering, and the grey economy. (Nitpicking: if GDP got down by 0.5% one year and up by 0.5% the year after, this does not exactly compensate!) Chapter 5 reflects upon the importance of definitions and boundaries in creating official statistics and categorical data. A chapter (Chap 6) on the gathering of data in the past (read prior to the “Big Data” explosion) is preparing the ground to the chapter on the current setting. Mostly about surveys, presented as definitely from the past, “shadows of their old selves”. And with anecdotes reminding me of my only experience as a survey interviewer (on Xmas practices!). About administrative data, progressively moving from collected by design to available for any prospection (or “farming”). A short chapter compared with the one (Chap 7) on new data (types), mostly customer, private sector, data. Covering the data accumulated by big tech companies, but not particularly illuminating (with bar-room remarks like “Facebook users tend to portray their lives as they would like them to be. Google searches may reflect more truthfully what people are looking for.”)
The following Chapter 8 is somehow confusing in its defence of microdata, by which I understand keeping the raw data rather than averaging through summary statistics. Synthetic data is mentioned there, but without reference to a reference model, while machine learning makes a very brief appearance (p.222). In Chapter 9, (statistical) data analysis is [at last!] examined, but mostly through descriptive statistics. Except for a regression model and a discussion of the issues around hypothesis testing and Bayesian testing making its unique visit, albeit confusedly in-between references to Taleb’s Black swan, Gödel’s incompleteness theorem (which always seem to fascinate authors of general public science books!), and Kahneman and Tversky’s prospect theory. Somewhat surprisingly, the chapter also includes a Taoist tale about the farmer getting in turns lucky and unlucky… A tale that was already used in What are the chances? that I reviewed two years ago. As this is a very established parable dating back at least to the 2nd century B.C., there is no copyright involved, but what are the chances the story finds its way that quickly in another book?!
The last and final chapter is about the future, unsurprisingly. With prediction of “plenty of black boxes“, “statistical lawlessness“, “data pooling” and data as a commodity (which relates with some themes of our OCEAN ERC-Synergy grant). Although the solution favoured by the author is centralised, through a (national) statistics office or another “trusted third party“. The last section is about the predicted end of theory, since “simply looking at data can reveal patterns“, but resisting the prophets of doom and idealising the Rise of the (AI) machines… The lyrical conclusion that “With both production consolidation and use of data increasingly in the ‘hands’ of machines, and our wise interventions, the more distant future will bring complete integrations” sounds too much like Brave New World for my taste!
“…the privacy argument is weak, if not hypocritical. Logically, it’s hard to fathom what data that we share with an online retailer or a delivery company we wouldn’t share with others (…) A naysayer will say nay.” (p.190)
The way the book reads and unrolls is somewhat puzzling to this reader, as it sounds like a sequence of common sense remarks with a Guesstimation flavour on the side, and tiny historical or technical facts, some unknown and most of no interest to me, while lacking in the larger picture. For instance, the long-winded tale on evaluating the cumulated size of a neighbourhood lawns (p.34-38) does not seem to be getting anywhere. The inclusion of so many warnings, misgivings, and alternatives in the collection and definition of data may have the counter-effect of discouraging readers from making sense of numeric concepts and trusting the conclusions of data-based analyses. The constant switch in perspective(s) and the apparent absence of definite conclusions are also exhausting. Furthermore, I feel that the author and his rosy prospects are repeatedly minimizing the risks of data collection on individual privacy and freedom, when presenting the platforms as a solution to a real time census (as, e.g., p.178), as exemplified by the high social control exercised by some number savvy dictatures! And he is highly critical of EU regulations such as GDPR, “less-than-subtle” (p.267), “with its huge impact on businesses” (p.268). I am thus overall uncertain which audience this book will eventually reach.
[Disclaimer about potential self-plagiarism: this post or an edited version will potentially appear in my Books Review section in CHANCE.]
don’t wear your helmet, you could have a bike accident!
Posted in Kids, Running, Statistics, Travel with tags Argentina, Australia, bicycle, Canada, cycling regulation, France, helmet, New Zealand, rental bike, Senate, survey, Vélib on January 18, 2022 by xi'an
As once in a while reappears the argument that wearing a bike helmet increases one’s chances of a bike accident. In the current case, it is to argue against a French regulation proposal that helmets should be compulsory for all cyclists. Without getting now into the pros and cons of compulsory helmet laws (enforced in Argentina, Australia, and New Zealand, as well as some provinces of Canada), I see little worth in the study cited by Le Monde towards this argument. As the data is poor and poorly analysed. First, there is a significant fraction of cycling accidents when the presence of an helmet is unknown. Second, the fraction of cyclists wearing helmets is based on a yearly survey involving 500 persons in a few major French cities. The conclusion that there are three times more accidents among cyclists wearing helmets than among cyclists not wearing helmets is thus not particularly reliable. Rather than the highly debatable arguments that (a) seeing a cyclist with an helmet would reduce the caution of car or bus drivers, (b) wearing an helmet would reduce the risk aversion of a cyclist, (c) sport-cyclists are mostly wearing helmets but their bikes are not appropriate for cities (!), I would not eliminate [as the authors do] the basic argument that helmeted cyclists are on average traveling longer distances. With a probability of an accident that necessarily increases with the distance traveled. While people renting on-the-go bikes are usually biking short distances and almost never wear helmets. (For the record, I mostly wear a [bright orange] helmet but sometimes do not when going to the nearby bakery or swimming pool… Each time I had a fall, crash or accident with a car, I was wearing an helmet and I once hit my head or rather the helmet on the ground, with no consequence I am aware of!)
