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Statistical in sentence

291 examples of Statistical in a sentence

Oddly, when comparing acupuncture with sham acupuncture, the statistical difference concerning death or the need for institutional care was small.
For example, many trials did not describe their method of measuring statistical relevance, and only called themselves “randomized controlled trials.”
But no one should be fooled by statistical legerdemain: Focusing on real national income data during a period of deflation is merely an effort to repackage an economic depression as a great success story.
Despite decades of careful data collection and mathematical and statistical research, on many large questions we have little more than rules of thumb.
Theoretical biology's models will be formulated not in a few simple equations, but in a complex of algorithms, statistical analyses, and simulations.
The British government uses a figure of a little more than £1 million as a general limit to the amount it is prepared to pay to save a statistical life – for example, by improving road safety.
The commodity boom and discovery of mineral resources in fragile states have sown seeds of discord, while the spread of democracy in low-income countries – perhaps surprisingly – increases the statistical likelihood of political violence.
The Reference Policy Rule is essentially the rule first proposed in 1993 by John Taylor of Stanford University, based on his statistical estimate of what the Fed appeared to have been doing under Paul Volcker and Alan Greenspan during a period of both low inflation and low unemployment.
An even more spectacular example of a statistical error and sleight of hand is the widely cited claim of Harvard economists Carmen Reinhart and Kenneth Rogoff that countries’ growth slows sharply if their debt/GDP ratio exceeds 90%.
Using advanced statistical and mapping methods, they identified how these various threats are arrayed around the world.
Using his low death rate as a base, Séralini claimed – with no statistical analysis – that the higher death rate (just below 40%) for the remaining 180 rats fed with GM corn and Roundup was suspicious.
Eighteen months ago, French President Nicolas Sarkozy established an international Commission on the Measurement of Economic Performance and Social Progress, owing to his dissatisfaction – and that of many others – with the current state of statistical information about the economy and society.
But changes in society and the economy may have heightened the problems, at the same time that advances in economics and statistical techniques may have provided opportunities to improve our metrics.
Nowadays, people move faster than statistical offices can count them.
His mapping and statistical analysis helped identify the epicenter of the outbreak and thus its root cause.
As indicated in the report A World that Counts: Mobilizing the Data Revolution for Sustainable Development, we must modernize the practices used by statistical offices and other public agencies, while tapping into new sources of data in a thoughtful and creative way that complements traditional approaches.
The new partnership aims to strengthen data collection and monitoring efforts by raising more funds, encouraging knowledge-sharing, addressing key barriers to access and use of data, and identifying new big-data strategies to upgrade the world’s statistical systems.
Decades of frustration are now yielding to success: the application of sophisticated statistical techniques to the accelerating accumulation of unprecedented quantities of so-called Big Data that the Internet simultaneously generates and captures.
Even now, I hear axes being ground, and statistical models being recalculated.
Instead, several researchers have relied upon the data in older studies to calculate the statistical probability of inflicted brain injury when certain features (such as intracranial hemorrhage, retinal hemorrhage, brain swelling, and seizures) are present.
Recently developed statistical tools have finally made this possible.
He would have relished the work of psychologists showing that we have a confirmation bias (we favor and remember information that supports, rather than contradicts, our beliefs); that we systematically overestimate the accuracy of our beliefs (the overconfidence effect); and that we have a propensity to respond to the plight of a single identifiable individual rather than a large number of people about whom we have only statistical information.
Even the use of the normal scientific method no longer works; given so many measurements, and so many more potential connections among what’s being measured, our standard statistical tools generate nonsensical results.
Its popularity is based on the fact that it addresses key problems in statistical inference.
The statistical conventions on which we base our estimates were adopted a half-century ago, at a time when the economy was producing relatively similar physical goods.
In light of these challenges, UK Chancellor of the Exchequer George Osborne asked me ten months ago to assess the United Kingdom’s current and future statistical needs.
While free virtual goods clearly have value to consumers, they are entirely excluded from GDP, in accordance with internationally accepted statistical standards.
The traditional statistical distinction between productive firms and consuming households leaves little room to account for households as value creators.
Consequently, internationally agreed statistical standards will almost always be somewhat out of date or incomplete, as they are bound to lag behind changes in the economy.
National statistical offices should explore measurement issues that go beyond the prevailing standards, rather than use compliance as an excuse for their failure to innovate.
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