This link has been bookmarked by 109 people . It was first bookmarked on 18 Mar 2010, by Jason Buell.
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akarim aAbout the wrong intepratation of statistics
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"It’s science’s dirtiest secret: The “scientific method” of testing hypotheses by statistical analysis stands on a flimsy foundation. Statistical tests are supposed to guide scientists in judging whether an experimental result reflects some real effect or is merely a random fluke, but the standard methods mix mutually inconsistent philosophies and offer no meaningful basis for making such decisions. Even when performed correctly, statistical tests are widely misunderstood and frequently misinterpreted. As a result, countless conclusions in the scientific literature are erroneous, and tests of medical dangers or treatments are often contradictory and confusing."
By Tom Siegfried at Science News on March 27, 2010. -
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Saul YusufzaiScience fails to face the shortcomings of statistics
Science News Science Magazine Nature Breaking News The Society for Science and the Public
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22 Mar 10
Lawrence BeckScience fails to face the shortcomings of statistics
Science News Science Magazine Nature Breaking News The Society for Science and the Public
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Maggie DumisaniScience fails to face the shortcomings of statistics
Science News Science Magazine Nature Breaking News The Society for Science and the Public
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21 Mar 10
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20 Mar 10
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patrick andrewsCorrectly phrased, experimental data yielding a P value of .05 means that there is only a 5 percent chance of obtaining the observed (or more extreme) result if no real effect exists (that is, if the no-difference hypothesis is correct).
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Carol FurchnerThe problems of relying on statistical analysis in evaluating scientific data
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19 Mar 10
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Mark O'SullivanA useful article outlining the shortcomings of statistics when it comes to ascertaining scientific fact. Half of all medical data could be wrong. "For better or for worse, science has long been married to mathematics. Generally it has been for the better.
science statistics medicine research homeopathy math mathamatics probablility stats news doubleblind tests CAM
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During the past century, though, a mutant form of math has deflected science’s heart from the modes of calculation that had long served so faithfully. Science was seduced by statistics, the math rooted in the same principles that guarantee profits for Las Vegas casinos. Supposedly, the proper use of statistics makes relying on scientific results a safe bet. But in practice, widespread misuse of statistical methods makes science more like a crapshoot.
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Statistical significance is a phrase that every science graduate student learns, but few comprehend.
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What eventually emerged was a hybrid mix of the mutually inconsistent Fisher and Neyman-Pearson approaches, which has rendered interpretations of standard statistics muddled at best and simply erroneous at worst.
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A well-fed dog may seldom bark, but observing the rare bark does not imply that the dog is hungry. A dog may bark 5 percent of the time even if it is well-fed all of the time.
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Statisticians perpetually caution against mistaking statistical significance for practical importance, but scientific papers commit that error often. Ziliak studied journals from various fields — psychology, medicine and economics among others — and reported frequent disregard for the distinction.
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When several drugs are tested at once, or a single drug is tested on several groups, chances of getting a statistically significant but false result rise rapidly.
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the 95 percent confidence calculation is based on the same math as the .05 P value and so still shares some of its problems
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Statistical problems also afflict the “gold standard” for medical research, the randomized, controlled clinical trials that test drugs for their ability to cure or their power to harm.
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Bayesian math seems baffling at first, even to many scientists, but it basically just reflects the need to include previous knowledge when drawing conclusions from new observations.
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In many real-life contexts, Bayesian methods do produce the best answers to important questions.
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But Bayesian methods introduce a confusion into the actual meaning of the mathematical concept of “probability” in the real world. Standard or “frequentist” statistics treat probabilities as objective realities; Bayesians treat probabilities as “degrees of belief” based in part on a personal assessment or subjective decision about what to include in the calculation. That’s a tough placebo to swallow for scientists wedded to the “objective” ideal of standard statistics.
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“What does probability mean in real life?” the statistician David Salsburg asked in his 2001 book The Lady Tasting Tea. “This problem is still unsolved, and ... if it remains unsolved, the whole of the statistical approach to science may come crashing down from the weight of its own inconsistencies.”
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Gustavo Lacerda"Science fails to face the shortcomings of statistics"
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18 Mar 10
Tip NgIt’s science’s dirtiest secret: The “scientific method” of testing hypotheses by statistical analysis stands on a flimsy foundation. Statistical tests are supposed to guide scientists in judging whether an experimental result reflects some real effect or
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The “scientific method” of testing hypotheses by statistical analysis stands on a flimsy foundation. Statistical tests are supposed to guide scientists in judging whether an experimental result reflects some real effect or is merely a random fluke, but the standard methods mix mutually inconsistent philosophies and offer no meaningful basis for making such decisions. Even when performed correctly, statistical tests are widely misunderstood and frequently misinterpreted. As a result, countless conclusions in the scientific literature are erroneous, and tests of medical dangers or treatments are often contradictory and confusing.
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Rashid Al-YahyaiHow science statistical significance works!
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Correctly phrased, experimental data yielding a P value of .05 means that there is only a 5 percent chance of obtaining the observed (or more extreme) result if no real effect exists (that is, if the no-difference hypothesis is correct)
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This means that it is 95 percent certain that the observed difference between groups, or sets of samples, is real and could not have arisen by chance.
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Statisticians perpetually caution against mistaking statistical significance for practical importance, but scientific papers commit that error often. Ziliak studied journals from various fields — psychology, medicine and economics among others — and reported frequent disregard for the distinction.
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Many researchers now also commonly report results with confidence intervals, similar to the margins of error reported in opinion polls.
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guyo123Science fails to face the shortcomings of statistics
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niscyyScience fails to face the shortcomings of statistics
statistics science mathematics bayesian research interesting for:7eddi
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Antoine BertierThe goal of this project is the study of a novel design approach to hardware implementation for testing and using the capability of self-assembling, self-organising, and metamorphosis of robotic systems. Such an approach finds its theoretical roots on recent studies in swarm intelligence, i.e., in studies of self-organising and self-assembling capabilities shown by social animals.
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That interpretation commits an egregious logical error (technical term: “transposed conditional”): confusing the odds of getting a result (if a hypothesis is true) with the odds favoring the hypothesis if you observe that result. A well-fed dog may seldom bark, but observing the rare bark does not imply that the dog is hungry. A dog may bark 5 percent of the time even if it is well-fed all of the time.
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17 Mar 10
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