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- In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution. This holds even if the original variables themselves are not normally distributed.
en.wikipedia.org/wiki/Central_limit_theorem
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中心极限定理 （英语：central limit theorem，簡作 CLT ）是 概率论 中的一组定理。 在概率论中，中心极限定理 (CLT) 确认，在许多情况下，对于独立并同样分布的随机变量，即使原始变量本身不是 正态分布 ，标准化样本均值的抽样分布也趋向于标准 正态分布. 这组定理是 数理统计学 和 误差 分析的理论基础，指出了大量随机变量之和近似服从 正态分布 的条件。 历史. Tijms (2004, p.169) 写到： 棣莫佛-拉普拉斯定理. 用正态分布逼近二项分布.
In probability theory, the central limit theorem ( CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution. This holds even if the original variables themselves are not normally distributed.
2022年7月6日 · The central limit theorem states that if you take sufficiently large samples from a population, the samples’ means will be normally distributed, even if the population isn’t normally distributed. Example: Central limit theorem. A population follows a Poisson distribution (left image).
- In a normal distribution , data are symmetrically distributed with no skew. Most values cluster around a central region, with values tapering off a...
- The three types of skewness are: Right skew (also called positive skew ) . A right-skewed distribution is longer on the right side of its peak than...
- Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, conveni...
2018年10月29日 · The central limit theorem in statistics states that, given a sufficiently large sample size, the sampling distribution of the mean for a variable will approximate a normal distribution regardless of that variable’s distribution in the population. Unpacking the meaning from that complex definition can be difficult.
中央極限定理（英語：central limit theorem，簡作 CLT）是機率論中的一組定理。 在機率論中，中央極限定理 (CLT) 確認，在許多情況下，對於獨立並同樣分布的隨機變數，即使原始變量本身不是 常態分布 ，標準化樣本均值的抽樣分布也趨向於標準 常態分布 .
2023年2月24日 · Central limit theorem (CLT) is a statistical theory given that as sample sizes get larger, the mean of all samples will be approximately equal to the mean of the population, and the distribution of means will approach normality. Let’s take a closer look at how CLT works to gain a better understanding.
2024年4月22日 · The central limit theorem (CLT) states that the distribution of sample means approximates a normal distribution as the sample size gets larger, regardless of the population's...
