我們處在后真相時(shí)代，當(dāng)你看新聞恬汁，聽(tīng)到別人講故事時(shí)壕曼，請(qǐng)認(rèn)真想一想，它是不是真的瞭恰？
We accept a story uncritically if it confirms what we'd like to be true. And we reject any story that contradicts it.
我們會(huì)不加批判的接受一個(gè)故事，當(dāng)它證實(shí)了我們認(rèn)為是真實(shí)的故事時(shí)狱庇，并且我們會(huì)拒絕與之相悖的故事惊畏。
A single story is meaningless and misleading unless it's backed up by large-scale data. But even if we had a large-scale data, that might still not be enough. Because it could still be consistent with rival theories.
但一個(gè)簡(jiǎn)單的故事時(shí)沒(méi)有意義且誤導(dǎo)人的，除非它有大量的數(shù)據(jù)支持密任。但即便我們有大量的數(shù)據(jù)颜启，這可能仍然不夠。因?yàn)樗赡芘c對(duì)立結(jié)論一致浪讳。
Data is just a collection of facts. Evidence is a data that supports one theory and rules out others. So the best way to support your theory is actually to try to disprove it, to play devil's advocate.
數(shù)據(jù)只是事實(shí)的組合缰盏。證據(jù)是支持一種理論，排除其他理論的數(shù)據(jù)淹遵。所以支持你的理論最好的方法是口猜，試圖去反駁它，做魔鬼的代言人（唱反調(diào)）透揣。
We should ask the following: If it's a story, is it true? If it's true, is it backed up by large-scale evidence? If it is, who is it by, what are their credentials? Is it published, how rigorous is the journal?
反過(guò)來(lái)暮的，我們應(yīng)該問(wèn)：如果這是個(gè)故事，這是真的嗎淌实？如果這是真的冻辩，有大量的證據(jù)支持嗎？如果有拆祈，證據(jù)是誰(shuí)提供的恨闪，他們的憑證是什么？它發(fā)表了嗎放坏？這個(gè)期刊是否足夠權(quán)威咙咽？
And ask yourself the million-dollar question: If the same study was written by the same authors with the same credentials but found the opposite results, would you still be willing to believe it and share it?
并且鄭重的問(wèn)自己，如果同樣的研究淤年，由同等資質(zhì)的作者所寫(xiě)钧敞，卻得出相反的結(jié)論蜡豹，你還愿意相信并分享它嗎？
Only if it's true can it be fact. Only if it's representative can it be data. Only if it's supportive can it be evidence. And only with evidence can we move from a post-truth world to a pro-truth world.
只有它是真的溉苛，才能成為事實(shí)镜廉。只有具有代表性，才能成為數(shù)據(jù)愚战。只有提供支持娇唯，才能成為證據(jù)。只有是證據(jù)寂玲，我們才能從后真相世界走向支持真相的世界塔插。
