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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.
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Original Submission
(Score: 0) by Anonymous Coward on Friday June 19 2015, @06:03PM
Thanks for the response, I will read you post in more detail later. For now I note that you seem to be assuming that correlations are rare (eg between stepping on cracks and broken backs). Common sense tells me that, where there are more cracks to step on, people are more likely to trip and have broken backs. So I consider your null hypothesis to be implausible. Rejecting it offers no information about the validity of your original theory.
This assumption that most things are uncorrelated is dead wrong in the case of living things. Everything is cycling daily, monthly, yearly, etc (check out human birth rates, death rates). The default assumption should be that everything is correlated with each other, if only via the earth's cycles.
Finding a correlation tells you nothing more than that you spent enough money to get enough data to see it. Your theory needs to predict a specific relationship with a certain shape/magnitude/whatever it is you think. A theory that can only predict "some relationship" is worthless.