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Dramatic increases in data science education coupled with robust evidence-based data analysis practices could stop the scientific research reproducibility and replication crisis before the issue permanently damages science's credibility, asserts Roger D. Peng in an article in the newly released issue of Significance magazine.
"Much the same way that epidemiologist John Snow helped end a London cholera epidemic by convincing officials to remove the handle of an infected water pump, we have an opportunity to attack the crisis of scientific reproducibility at its source," wrote Peng, who is associate professor of biostatistics at the Johns Hopkins Bloomberg School of Public Health.
In his article titled "The Reproducibility Crisis in Science"—published in the June issue of Significance, a statistics-focused, public-oriented magazine published jointly by the American Statistical Association (ASA) and Royal Statistical Society—Peng attributes the crisis to the explosion in the amount of data available to researchers and their comparative lack of analytical skills necessary to find meaning in the data.
"Data follow us everywhere, and analyzing them has become essential for all kinds of decision-making. Yet, while our ability to generate data has grown dramatically, our ability to understand them has not developed at the same rate," he wrote.
This analytics shortcoming has led to some significant "public failings of reproducibility," as Peng describes them, across a range of scientific disciplines, including cancer genomics, clinical medicine and economics.
The original article came from phys.org.
[Related]: Big Data - Overload
Original Submission
(Score: 2) by kaszz on Friday June 19 2015, @02:59PM
Why use PRNG? Especially if the lab is CERN? Why not use a hardware random number generator? there's several methods to accomplish a such device with good performance.
(and the guy not accepting code due French vs British.. time for LART and clue bat ;-) )
(Score: 3, Informative) by maxwell demon on Friday June 19 2015, @05:50PM
Repeatability.
You want to be able to do the exact same calculation again. Which means you need to use the very same sequence of "random" numbers again.
With a PRNG, you can get that by using the same seed again. With a true randomness source, the only way to do it is to store the complete sequence of random numbers you used.
The Tao of math: The numbers you can count are not the real numbers.
(Score: 2) by MichaelDavidCrawford on Saturday June 20 2015, @03:02AM
My particular experiment had a piece of uranium with a geiger counter down in the beam tunnel.
Yes that works very well but why did they need the uranium? Why not just cosmic rays and some plastic scintillator.
Yes I Have No Bananas. [gofundme.com]