So I would guess that it is roughly, normally distributed.

所以我猜它大致呈正态分布。

And each of them are independent, standard, normal, normally distributed random variables.

而且每一个都是独立的、标准的、正态的、正态分布的随机变量。

All of Z's are normally distributed.

所有 Z 都服从正态分布。

Even this approximation will be approximately normally distributed.

即使这个近似值也将近似服从正态分布。

So our distribution, they're telling us that it's normally distributed.

所以我们的分布，他们告诉我们它是正态分布的。

Or another way to think about it is this will be normally distributed if our sample size is greater than 30.

或者另一种思考方式是，如果我们的样本量大于 30，这将服从正态分布。

They're saying it's normally distributed, so that's as good of a bell curve as I'm capable of drawing.

他们说它是正态分布的，所以这是我能画的最好的钟形曲线。

Let's say I have another random variable, Q2, that is defined as-- let's say I have one independent, standard, normally distributed variable.

假设我有另一个随机变量 Q2，它被定义为-- 假设我有一个独立的、标准的、正态分布的变量。

We just said if this thing is well over 30, or at least 30, then this value right here, this statistic, is going to be normally distributed.

我们刚刚说过， 如果这个东西远远超过 30，或者至少 30， 那么这里的这个值， 这个统计数据， 将服从正态分布。

So they're saying, the 2007 AP statistics examination scores were not normally distributed with a mean of 2.8 and a standard deviation of 1.34.

所以他们说，2007 年 AP 统计考试分数不是正态分布的， 均值为 2. 8，标准差为 1. 34。

So we could be talking about molecular interactions, and every time compound x interacts with compound y, what might result doesn't have to be normally distributed.

所以我们可以谈论分子相互作用，每次化合物 x 与化合物 y 相互作用时， 可能产生的结果不一定是正态分布的。

The grades on a statistics midterm for a high school are normally distributed with a mean of 81 and a standard deviation of 6.3.

一所高中统计期中考试的成绩呈正态分布，均值为 81，标准差为 6.3。

So you could go to their site and actually look up these same problems. So this first problem, which of the following data sets is most likely to be normally distributed?

所以你可以去他们的网站并实际查找这些相同的问题。 那么这第一个问题，下面哪个数据集最有可能服从正态分布？

Now, if your sample size is less than 30, especially if it's a good bit less than 30, all of a sudden this expression will not be normally distributed.

现在，如果您的样本量小于 30，特别是如果它比 30 少很多， 突然间这个表达式将不会服从正态分布。

But what happens is, if you take a sum of a ton of those interactions, then, all of a sudden, the end result will be normally distributed.

但是发生的事情是，如果你对大量这些交互进行求和， 那么，突然之间，最终结果将呈正态分布。

The height of fifth grade boys in the United States is approximately normally distributed-- that's good to know-- with a mean height of 143.5 centimeters.

美国五年级男生的身高大致呈正态分布——知道这一点很好——平均身高为 143.5 厘米。

So let's say I have the random variable X. If X is normally distributed, we could write that X is a normal random variable with a mean of 0 and a variance of 1.

假设我有随机变量 X。如果 X 服从正态分布，我们可以写成 X 是均值为 0 方差为 1 的正态随机变量。

But I would say-- the other choices explain why you believe they would not follow-- or they said, which of the following data sets is most likely to be normally distributed.

但我想说的是——其他选择解释了为什么你认为他们不会遵循——或者他们说，以下哪一组数据最有可能呈正态分布。

And since we're only taking one sum over here-- we're only taking the sum of one independent, normally distributed, standard or normally distributed variable, we say that this only has 1 degree of freedom.

由于我们在这里只取一个总和——我们只取一个独立的、正态分布的、标准的或正态分布的变量的总和，我们说这只有 1 个自由度。

And so this whole thing is going to be approximately normally distributed, and so you can use a Z-table to figure out the probability of getting a result at least that extreme.

因此， 整个事情将大致服从正态分布，因此您可以使用 Z 表来计算至少获得该极端结果的概率。