Many human traits, like height, follow a normal distribution.

许多人类特质，例如身高，就符合常态分布。

What makes the distribution normal is that it follows this curved shape.

分布普不普通，就取决于其是否为钟型曲线。

So this is going to be a normal distribution.

所以这将是一个正态分布。

And we know that that is going to be a normal distribution.

我们知道这将是正态分布。

We're going to get some normal distribution.

我们将获得一些正态分布。

So this distribution, once again, we don't know whether it's a normal distribution or not.

所以这个分布，再一次，我们不知道它是否是正态分布。

So it still won't be a normal distribution when n is equal to 2.

所以当 n 等于 2 时，它仍然不是正态分布。

So this is a normal distribution right over there.

所以这是那边的正态分布。

This is actually going to be a normal distribution regardless of-- this one just has a well-defined standard deviation mean.

这实际上将是一个正态分布，无论如何 - 这个只是有一个明确定义的标准偏差平均值。

So as that approaches infinity, your actual sampling distribution of the sample mean will approach a normal distribution.

因此，当它接近无穷大时，样本均值的实际抽样分布将接近正态分布。

When n is pretty small, it doesn't approach a normal distribution that well.

当 n 非常小时，它不会很好地接近正态分布。

So my guess is it definitely wouldn't be a normal distribution.

所以我的猜测是它肯定不会是正态分布。

For the other choices, explain why you believe they would not follow a normal distribution.

对于其他选择，请解释您认为它们不服从正态分布的原因。

It has a slightly less kurtosis, which means it's closer to being a normal distribution than n is equal to 16.

它的峰度略低，这意味着它比 n 等于 16 更接近于正态分布。

So notice when you took an n of 2, even though we did it 10,000 times, this is not approaching a normal distribution.

所以请注意当你取 n 为 2 时，即使我们做了 10, 000 次， 这也没有接近正态分布。

So this is a cumulative distribution function for this same-- this is a normal distribution.

所以这是一个累积分布函数——这是一个正态分布。

You take the square root of that thing, and invert it, and that's the normal distribution.

你取那个东西的平方根，然后倒过来，这就是正态分布。

If we have a normal distribution-- let me draw a normal distribution.

如果我们有一个正态分布——让我画一个正态分布。

We're not going to assume it's a normal distribution because this is a bad estimate.

我们不会假设它是正态分布，因为这是一个错误的估计。

But to really make the point that you don't have to have a normal distribution, I like to use crazy ones.

但要真正说明你不必有正态分布，我喜欢使用疯狂的。