An inherent bias can color scientific results when researchers can see which samples they’re testing, according to a review of 900,000 experiments by a group of Australian biologists.
Non-blind experiments produced results 27 percent stronger than blind trials, according to the paper, published this week in the journal PLoS Biology.
“We found that non-blond papers tended to exaggerate differences between the experimental group and the control group,” said Luke Holman, the lead researcher from the Australian National University’s Research School of Biology. “For example, a non-blind trial of a new drug might conclude that it is way more effective than a placebo, when in fact the drug’s true effect is rather modest, simply because the researchers’ expectations biased the results.”
The team analyzed about 900,000 papers in the PubMed life sciences database, using automated “data mining.”
Blind data recording accounted for less than one in four of the total publications, they found.
They also compared 83 pairs of evolutionary biology papers on similar topics, squaring the blind- against the non-blind, they said.
“It is time for reviewers, editors and other assessors to insist on blind methods across the life sciences,” the authors concluded. “We perceive a tendency to regard working blind as an unnecessary nuisance, but the evidence suggests that blind protocols are vital to good research practice.”
The paper comes amid notices of investigations and dozens of retractions from several publishing houses. Forty-three papers were retracted by BioMed Central in November, due to a fraudulent peer-review process – and this week Hindawi Publishing Corporation announced it had found three editors who were using fake names to approve papers, 32 of which are now being re-reviewed.
The Australian National University team said straightening out ethical problems is paramount in the scientific fields.
“Science is still the best method we have for understanding the world, and we have to keep working to make it better,” said Megan Head, a co-researcher. “It is not necessarily slower to take data blind, you just need to be a little creative.”
Filed Under: Drug Discovery