From the influence of the father came modern inferential statistics, which is based squarely on theories of probability.

由于" 父亲" 的影响则产生了完全基于率的现代推统计学。

And this is actually what most of inferential statistics is all about.

这实际上大多数推统计的全部内容。

And " ostensive communication, " or " inferential communication, " is a shorthand.

要解这个定义，就需要搞清楚其中的关键念：“明示交际行动”和“最佳关联假定”。波伯和威尔逊同意格赖的观点：交际不只简单的编码和解码的过程，而且涉及推。但他们认为推只与听话人有关。从说话人方面讲，交际应该被看成一种表明自身说话意图的行为。他们称之为“明示行动”。换句话说，交际的完整“明示-推交际”，所谓“明示交际”或“推交际”只简称。

But in all of inferential statistics, I think it behooves you to become as familiar with this formula as possible.

但在所有推统计中，我认为你应该尽可能熟悉这个公式。

So hopefully you found that helpful, and we're going to build on this later on in some of our inferential statistics.

所以希望你觉得这很有帮助，我们将在稍后的一些推统计中以此为基础。

So in a lot of what we're doing in this inferential statistics, we're trying to figure out what is the probability of getting a certain sample mean.

所以在我们在这个推统计中所做的很多事情中，我们试图弄清楚获得某个样本均值的率多少。

Everything we do, or almost everything we do in inferential statistics, which is essentially making inferences based on data points, is to some degree based on the normal distribution.

我们所做的一切， 或者我们在推统计中所做的几乎所有事情，本质上根据数据点进行推， 在某种程度上都基于正态分布的。

And just so you know where we're going with this, the whole point of this is so that we can eventually do some inferential statistics about differences of means.

只为了让您知道我们的目标什么，这就我们最终可以对均值差异进行一些推性统计的目的。

" They're extremely rational and they're much better in some ways at doing inferential learning" – using patchy information to make accurate generalisations about a messy world – " than any other creature that we know of."

“婴儿非常性，在某些方面比我们所知的任何其他生物都更擅长推学习”——利用零碎的信息对混沌的世界做出准确的。