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it appears that there is a quite significant hole in the National Accounting (and thus the GDP statistics) around Internet related activities since most of this accounting is concerned with measuring the production and distribution of tangible products and the associated services. For the most part the available numbers don’t include many Internet (or “social capital” e.g. in health and education) related activities as they are linked to intangible outputs. The significance of not including social capital components in the GDP has been widely discussed elsewhere. The significance (and potential remediation) of the absence of much of the Internet related activities was the subject of the workshop.

there had been a series of critiques of GDP statistics from Civil Society (CS) over the last few years—each associated with a CS “movements—the Woman’s Movement and the absence of measurement of “women’s (and particularly domestic) work”; the Environmental Movement and the absence of the longer term and environmental costs of the production of the goods that the GDP so blithely counts as a measure of national economic well-being; and most recently with the Sustainability Movement, and the absence of measures reflective of the longer term negative effects/costs of resource depletion and environmental degradation. What I didn’t see anywhere apart from the background discussions to the OECD workshop itself were critiques reflecting issues related to the Internet or ICTs.

the implications of the limitations in the Internet accounting went beyond a simple technical glitch and had potentially quite profound implications from a national policy and particularly a CS and community based development perspective. The possible distortions in economic measurement arising from the absence of Internet associated numbers in the SNA (there may be some $750 BILLION a year in “value’ being generated by Internet based search alone!) lead to the very real possibility that macro-economic analysis and related policy making may be operating on the basis of inadequate and even fallacious assumptions.

Accuracy is a hard thing to measure because it’s a hard thing to define. There are subjective and objective errors, and no standard way of determining whether a reported fact is true or false

The last big study of mainstream reporting accuracy found errors (defined below) in 59% of 4,800 stories across 14 metro newspapers. This level of inaccuracy — where about one in every two articles contains an error — has persisted for as long as news accuracy has been studied, over seven decades now.

With the explosion of available information, more than ever it’s time to get serious about accuracy, about knowing which sources can be trusted. Fortunately, there are emerging techniques that might help us to measure media accuracy cheaply, and then increase it.

For to all those who have, more will be given, and they will have an abundance; but from those who have nothing, even what they have will be taken away. —Matthew 25:29

Sociologist Robert K. Merton was the first to publish a paper on the similarity between this phrase in the Gospel of Matthew and the realities of how scientific research is rewarded

Even if two researchers do similar work, the most eminent of the pair will get more acclaim, Merton observed—more praise within the community, more or better job offers, better opportunities. And it goes without saying that even if a graduate student publishes stellar work in a prestigious journal, their well-known advisor is likely to get more of the credit. 

The question that the audience member asked — and one that we tried to touch on a bit in the panel itself — was who has access to this raw data. Twitter doesn’t comment on who has full access to its firehose, but to Weil’s credit he was at least forthcoming with some of the names, including stalwarts like Microsoft, Google and Yahoo — plus a number of smaller companies.

In the case of Twitter, the company offers free access to its API for developers. The API can provide access and insight into information about tweets, replies and keyword searches, but as developers who work with Twitter — or any large scale social network — know, that data isn’t always 100% reliable. Unreliable data is a problem when talking about measurements and analytics, where the data is helping to influence decisions related to social media marketing strategies and allocations of resources.

One of the companies that has access to Twitter’s data firehose is Gnip. As we discussed in November, Twitter has entered into a partnership with Gnip that allows the social data provider to resell access to the Twitter firehose.This is great on one level, because it means that businesses and services can access the data. The problem, as noted by panelist Raj Kadam, the CEO of Viralheat, is that Gnip’s access can be prohibitively expensive.

Scientists these days tend to keep up a polite fiction that all science is equal. Except for the work of the misguided opponent whose arguments we happen to be refuting at the time, we speak as though every scientist's field and methods of study are as good as every other scientist's and perhaps a little better. This keeps us all cordial when it comes to recommending each other for government grants.

Why should there be such rapid advances in some fields and not in others? I think the usual explanations that we tend to think of - such as the tractability of the subject, or the quality or education of the men drawn into it, or the size of research contracts - are important but inadequate. I have begun to believe that the primary factor in scientific advance is an intellectual one. These rapidly moving fields are fields where a particular method of doing scientific research is systematically used and taught, an accumulative method of inductive inference that is so effective that I think it should be given the name of "strong inference." I believe it is important to examine this method, its use and history and rationale, and to see whether other groups and individuals might learn to adopt it profitably in their own scientific and intellectual work. In its separate elements, strong inference is just the simple and old-fashioned method of inductive inference that goes back to Francis Bacon. The steps are familiar to every college student and are practiced, off and on, by every scientist. The difference comes in their systematic application. Strong inference consists of applying the following steps to every problem in science, formally and explicitly and regularly: Devising alternative hypotheses; Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly is possible, exclude one or more of the hypotheses; Carrying out the experiment so as to get a clean result; Recycling the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain, and so on.

On any new problem, of course, inductive inference is not as simple and certain as deduction, because it involves reaching out into the unknown. Steps 1 and 2 require intellectual inventions, which must be cleverly chosen so that hypothesis, experiment, outcome, and exclusion will be related in a rigorous syllogism; and the question of how to generate such inventions is one which has been extensively discussed elsewhere (2, 3). What the formal schema reminds us to do is to try to make these inventions, to take the next step, to proceed to the next fork, without dawdling or getting tied up in irrelevancies.

Occurrences of crime are properly expressed as the number of incidences per 100,000 people. Total numbers are not informative on their own and it is very easy to manipulate an argument by cherry picking between a total number and a rate.  Beware of claims about crime that use raw incidence numbers. When a change in whole incidence numbers is observed, this might not have any bearing on crime levels at all, because levels of crime are dependent on population.

Whole Numbers versus Rates

Reliability Not every criminal statistic is equally reliable. Even though we have measures of incidences of crimes across types and subtypes, not every one of these statistics samples the actual incidence of these crimes in the same way. Indeed, very few measure the total incidences very reliably at all. The crime rates that you are most likely to encounter capture only crimes known and substantiated by police. These numbers are vulnerable to variances in how crimes become known and verified by police in the first place. Crimes very often go unreported or undiscovered. Some crimes are more likely to go unreported than others (such as sexual assaults and drug possession), and some crimes are more difficult to substantiate as having occurred than others.

This question has concerned western thinkers at least since Plato (5th-4th century BCE). It has also concerned thinkers in other cultural traditions: Confucius (6th-5th century BCE); China’s legalist tradition; and India’s Kautilya (4th-3rd century BCE). The perspective here is that of the contemporary democratic west.

The core purpose of the state is protection. This view would be shared by everybody, except anarchists, who believe that the protective role of the state is unnecessary or, more precisely, that people can rely on purely voluntary arrangements.

Contemporary Somalia shows the horrors that can befall a stateless society. Yet horrors can also befall a society with an over-mighty state. It is evident, because it is the story of post-tribal humanity that the powers of the state can be abused for the benefit of those who control it.

Measuring and assessing academic performance is now a fact of scientific life.

Yet current systems of measurement are inadequate. Widely used metrics, from the newly-fashionable Hirsch index to the 50-year-old citation index, are of limited use1

Existing metrics do not capture the full range of activities that support and transmit scientific ideas, which can be as varied as mentoring, blogging or creating industrial prototypes.

Statistics recognizes different measures of an "average," or central tendency. The mean is our usual concept of an overall average - add up the items and divide them by the number of sharers

The median, a different measure of central tendency, is the half-way point.

A politician in power might say with pride, "The mean income of our citizens is $15,000 per year." The leader of the opposition might retort, "But half our citizens make less than $10,000 per year." Both are right, but neither cites a statistic with impassive objectivity. The first invokes a mean, the second a median. (Means are higher than medians in such cases because one millionaire may outweigh hundreds of poor people in setting a mean; but he can balance only one mendicant in calculating a median).

In the realm of public policy, we live in an age of numbers.

do wehold an outsize belief in our ability to gauge complex phenomena, measure outcomes and come up with compelling numerical evidence? A well-known quotation usually attributed to Einstein is “Not everything that can be counted counts, and not everything that counts can be counted.” I’d amend it to a less eloquent, more prosaic statement: Unless we know how things are counted, we don’t know if it’s wise to count on the numbers.

The problem isn’t with statistical tests themselves but with what we do before and after we run them.

On September 18, 2007, a few dozen neuroscientists, psychiatrists, and drug-company executives gathered in a hotel conference room in Brussels to hear some startling news. It had to do with a class of drugs known as atypical or second-generation antipsychotics, which came on the market in the early nineties.

the therapeutic power of the drugs appeared to be steadily waning. A recent study showed an effect that was less than half of that documented in the first trials, in the early nineteen-nineties. Many researchers began to argue that the expensive pharmaceuticals weren’t any better than first-generation antipsychotics, which have been in use since the fifties. “In fact, sometimes they now look even worse,” John Davis, a professor of psychiatry at the University of Illinois at Chicago, told me.

LAST week Michele Bachmann, a Republican representative from Minnesota, introduced a bill to roll back efficiency standards for light bulbs, which include a phasing out of incandescent bulbs in favor of more energy-efficient bulbs. The “government has no business telling an individual what kind of light bulb to buy,” she declared.

But this opposition ignores another, more important bit of American history: the critical role that government-mandated standards have played in scientific and industrial innovation.

inventions alone weren’t enough to guarantee progress. Indeed, at the time the lack of standards for everything from weights and measures to electricity — even the gallon, for example, had eight definitions — threatened to overwhelm industry and consumers with a confusing array of incompatible choices.

The study’s authors argue that removing age restrictions from sites like Facebook might actually be the best way of improving child safety online.

Elisabeth Staksrud, from the University of Oslo and one of the report’s authors comments that: “since children often lie about their age to join ‘forbidden’ sites it would be more practical to identify younger users and to target them with protective measures.”

This flies in the face of what many see as a critical security wall  protecting children from cyber-crime on social networking sites. A report released in January by Internet security firm PandaLabs identified Facebook and Twitter as the sites which are most prone to security breaches. The danger is particularly accute when young children enter their real personal information on their profile. Though, as the new research indicates, children are already lying about their age to sign up for a profile. So from a safety standpoint, the most important measure for children to take is to refrain from entering real information such as their address or where they go to school.

Asia is not invulnerable to an environmental disaster on the scale of the BP spill. Singapore is a major refining center and transshipment point for crude oil shipments between markets in East Asia and the other oil-rich Gulf region. The quest for more plentiful and less expensive oil for fast-growing Asian economies has also brought a wave of offshore drilling from India and the Gulf of Thailand, to Vietnam and Bohai Bay, on the northeast coast of China.

Asian governments have become particularly enthralled with gross domestic product statistics for validation

Gross domestic product has come in for some particularly hard knocks since the global financial crisis, notably after a report last year whose co-author was Joseph E. Stiglitz, a Nobel laureate in economics, that said reliance on gross domestic product had blinded governments to the increasing risks in the world economy since 2004.

most of the worries about income inequality are bogus, but some are probably better grounded and even more serious than even many of their heralds realize.

In terms of immediate political stability, there is less to the income inequality issue than meets the eye. Most analyses of income inequality neglect two major points. First, the inequality of personal well-being is sharply down over the past hundred years and perhaps over the past twenty years as well. Bill Gates is much, much richer than I am, yet it is not obvious that he is much happier if, indeed, he is happier at all. I have access to penicillin, air travel, good cheap food, the Internet and virtually all of the technical innovations that Gates does. Like the vast majority of Americans, I have access to some important new pharmaceuticals, such as statins to protect against heart disease. To be sure, Gates receives the very best care from the world’s top doctors, but our health outcomes are in the same ballpark. I don’t have a private jet or take luxury vacations, and—I think it is fair to say—my house is much smaller than his. I can’t meet with the world’s elite on demand. Still, by broad historical standards, what I share with Bill Gates is far more significant than what I don’t share with him.

when average people read about or see income inequality, they don’t feel the moral outrage that radiates from the more passionate egalitarian quarters of society. Instead, they think their lives are pretty good and that they either earned through hard work or lucked into a healthy share of the American dream.

Perhaps like me, you watched the BBC Two Horizons program Monday night presented by Sir Paul Nurse, president of the Royal Society and Nobel Prize winning geneticist for his discovery of the genes of cell division.

James. He really believes there's some kind of mainstream science "warmist" conspiracy against the brave outliers who dare to challenge the consensus. He really believes that "climategate" is a real scandal. He fails to understand that it is a common practice in statistics to splice together two or more datasets where you know that the quality of data is patchy. In the case of "climategate", researchers found that indirect temperature measurements based on tree ring widths (the tree ring temperature proxy) is consistent with other proxy methods of recording temperature from before the start of the instrumental temperature record (around 1950) but begins to show a decline in temperature after that for reasons which are unclear. Actual temperature measurements however show the opposite. The researcher at the head of the climategate affair, Phil Jones, created a graph of the temperature record to include on the cover of a report for policy makers and journalists. For this graph he simply spliced together the tree ring proxy data up until 1950 with the recorded data after that using statistical techniques to bring them into agreement. What made this seem particularly dodgy was an email intercepted by a hacker in which Jones referred to this practice as a "Mike's Nature trick", referring to a paper published by his colleague Mike Hulme Michael Mann in the journal Nature. It is however nothing out of the ordinary. Delingpole and others have talked about how this "trick" was used here to "hide the decline" revealed by the other dataset, as though this was some sort of deception. The fact that all parties were found to have behaved ethically is simply further evidence of the global warmist conspiracy. Delingpole takes it further and casts aspersions on scientific consensus and the entire peer review process.

When Nurse asked Delingpole the very straightforward question of whether he would be willing to trust a scientific consensus if he required treatment for cancer, he could have said "Gee, that's an interesting question. Let me think about that and why it's different."