T 分布的 90%
90% of the T Distribution

原始链接: https://entropicthoughts.com/ninety-percent-of-the-t-distribution

要评估一个结果是否真正卓越,你必须理解该过程固有的变异性,而不仅仅是将其与单一的前期数据进行比较。 当面对孤立的数据点时,人类的直觉往往会导致我们将正常的波动误读为重大的成就。例如,如果告诉你 49 这个结果是继 43 和 47 之后得出的,它看起来可能令人印象深刻。然而,通过分析这些典型结果,你可以计算出该过程的变异性。在这个例子中,四的极差意味着标准差约为五。因此,49 距离中点 45 不到一个标准差——这是一个统计学上的正常结果。 归根结底,语境至关重要。如果不评估标准差,你就无法确定一个结果是异常值,还是仅仅处于预期波动范围内。通过计算数据的离散程度,而不是依赖直觉,避免陷入“惊人数字”的陷阱。

这篇 Hacker News 讨论帖围绕一篇关于 T 分布的博文展开。读者们批评了作者的术语使用,指出其混淆了标准差与标准误,并厘清了“样本”与“样本量”之间的区别。 在技术讨论之余,作者(kqr)邀请读者参与一项关于键盘延迟的研究项目,旨在调查不同硬件品牌与类型之间是否存在性能差异。 此外,当一名用户请求帮助解读作者“关于”页面中一句关于其妻子的诗意描述时,该讨论串转向了哲学探讨。评论者们普遍认为,这段表述真诚而谦逊地表达了作者深深的感激与钦佩,形容这份爱如此深沉,以至于作者感到自己无法企及它的广度或独特性。作者本人也加入了对话,并对关于其原文中“程度”一词解读的玩笑做出了幽默的回应。
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原文

I’m sure you’ve been in a situation where someone has asked something like “Is 49 litres a good result?”

You don’t know, of course, so you ask “Compared to what?”

Maybe they respond “Compared to 43 litres!”

That sounds impressive, but you don’t want me to chastise you, so you say, “That still tells me nothing because I don’t know the variation inherent in the process. Give me another typical result!”

They might then say “Uhh, 47 litres.”

Now you let your guard down and think, “Oh, 49 is above both the typical results. Very good!”

And then i chastise you!

So you turn on your brain instead.

You have received two typical numbers: 43 and 47. These don’t tell you much about how the inherent variation, but they do tell you a little. The distance between them is four. If we multiply that by 1.3, we get our estimation of the standard deviation, which is something like 5 litres. That means 49 litres is less than one standard deviation away from the midpoint of 45 litres. That’s a normal result, not unusually good or bad.

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