就像正義一樣，科學(xué)在盲目的時候最有效

Like justice, science works best when it's blind. For the better part of two centuries, that's been the mantra for sound experimental design.

就像正義一樣，科學(xué)在盲目的時候最有效。在兩個世紀(jì)的大部分時間里，這一直是聲音實驗設(shè)計的口頭禪。

Hiding observations that risk introducing bias defines a standard of reliability in research. But a group of UK scientists have put forward an argument that in many cases, it could be a waste of effort, and one that might even do more harm than good.

在研究中，隱藏可能導(dǎo)致偏差的觀察結(jié)果定義了可靠性的標(biāo)準(zhǔn)。但一群英國科學(xué)家提出了一個觀點，在很多情況下，這可能是白費力氣，甚至可能弊大于利。

Together with colleagues from the University of Edinburgh and the Centre for Public Health in Belfast, clinical researcher Rohan Anand from Queen's University Belfast makes a case that scientists should think long and hard before working blinding procedures into their experiment.

來自貝爾法斯特女王大學(xué)的臨床研究員Rohan Anand與來自愛丁堡大學(xué)和貝爾法斯特公共衛(wèi)生中心的同事們一起提出了一個案例，即科學(xué)家們在對他們的實驗進(jìn)行盲法操作之前應(yīng)該仔細(xì)思考。

Their argument boils down to a question of cost versus benefit. We're quick to recognise the potential rewards of a blinded trial, but some of the less convenient consequences might mean it's not worth the fuss.

他們的爭論歸結(jié)為成本與收益的問題。我們很快意識到盲法試驗的潛在回報，但一些不太方便的結(jié)果可能意味著它不值得大驚小怪。

"Given that the number of new trials is increasing every year, with 25,000 registered since the start of 2019, we are concerned that a substantial amount of time, energy, and funding may be going into considering and implementing blinding without a sound rationale for it," Anand and his fellow researchers state in a recent Analysis article in The BMJ.

阿南德和他的研究伙伴在BMJ最近的一篇分析文章中說：“鑒于新的試驗數(shù)量每年都在增加，自2019年初以來已注冊了25000個，我們擔(dān)心大量的時間、精力和資金可能會在沒有合理理由的情況下考慮和實施盲法。”

That 'sound' rationale is all too easy to take for granted. After all, science evolved as a system of checks and balances to ensure our best ideas explaining the Universe weren't fanciful dreams born of peer pressure and wishful thinking.

這種“合理的”理由太容易被認(rèn)為是理所當(dāng)然的。畢竟，科學(xué)的發(fā)展是一種制衡體系，以確保我們解釋宇宙的最佳想法不是來自同輩壓力和一廂情愿的空想。

Along with replication in experimentation, positive and negative controls, p-values, and randomisation of test subjects, using naïve observers to report and measure variables is just one more way to ensure we don't confuse imagination for reason.

除了實驗中的復(fù)制、陽性和陰性對照、p值以及受試者的隨機(jī)性之外，使用天真的觀察者來報告和測量變量，只是確保我們不會因為理性而混淆想象力的另一種方法。

But none of these efforts come for free. Volunteers need to be recruited and screened, for instance, which as any postgrad knows is no easy task. Even then, they don't always stick around to the end.

但這些努力都不是免費的。例如，志愿者需要被招募和篩選，任何研究生都知道這不是一件容易的事情。即使這樣，他們也不總是堅持到底。