And the standard deviation--- this comes straight from the central limit theorem.

而标准偏差——这直接来自中心极限定理。

People might talk about the central limit theorem.

人们可能会谈论中心极限定理。

And that is a neat thing about the central limit theorem.

这就是中心极限定理的妙处。

Because I want to show you the power of the central limit theorem.

因为我想向你展示中心极限定理的威力。

The central limit theorem would have still applied.

中心极限定理仍然适用。

So this is what's super cool about the central limit theorem.

所以这就是中心极限定理的超级酷之处。

And that's the central limit theorem.

这就是中心极限定理。

But this is really one of the most important or interesting things about our universe-- central limit theorem.

但这确实是我们宇宙中最重要或最有趣的事情之一——中心极限定理。

The central limit theorem says as n approaches, really as it approaches infinity, then is when you get the real normal distribution.

中心极限定理说当 n 接近时，实际上当它接近无穷大时，就是当你得到真正的正态分布时。

So the central limit theorem, although I said you do a bunch of trials, it'll look like a normal distribution, definitely doesn't work for n equals 1.

所以中心极限定理，虽然我说你做了一堆试验， 它看起来像一个正态分布， 但绝对不适用于 n 等于 1。

Because we're going to assume, when we go to the next level, that when we take the samples, we're taking enough samples that the central limit theorem will actually apply.

因为我们要假设， 当我们进入下一个级别时，当我们采集样本时， 我们采集了足够的样本， 以至于中心极限定理将实际应用。

The variance of this distribution by the central limit theorem is going to be the variance of this distribution up here, which is P1 times 1 minus P1 over our sample size, over 1,000.

根据中心极限定理得出的该分布的方差将是此处分布的方差，即 P1 乘以 1 减去我们样本量超过 1, 000 的 P1。