sample mean

简明释义

样本均值

英英释义

例句

1.Researchers often use the sample mean 样本均值 to estimate the population mean when it's impractical to measure every individual.

研究人员通常使用样本均值 样本均值来估计总体均值，当测量每个个体不切实际时。

2.The sample mean 样本均值 of the test scores was calculated to determine the overall performance of the class.

测试分数的样本均值 样本均值被计算出来，以确定班级的整体表现。

3.In a survey, the sample mean 样本均值 income was found to be significantly higher than the national average.

在一项调查中，发现样本均值 样本均值收入显著高于全国平均水平。

4.To compare the effectiveness of two teaching methods, we calculated the sample mean 样本均值 of student grades for each method.

为了比较两种教学方法的有效性，我们计算了每种方法学生成绩的样本均值 样本均值。

5.The sample mean 样本均值 can be affected by outliers, so it's important to analyze the data carefully.

由于异常值可能影响样本均值 样本均值，因此仔细分析数据非常重要。

作文

In the field of statistics, understanding the concept of sample mean is crucial for data analysis and interpretation. The sample mean, or '样本均值' in Chinese, refers to the average value of a subset of data drawn from a larger population. This measure provides insights into the characteristics of the population without needing to analyze every single data point. To illustrate the importance of the sample mean, let’s consider a practical example. Suppose a researcher wants to understand the average height of adult men in a city. It would be impractical to measure the height of every man in the city due to time and resource constraints. Instead, the researcher can take a random sample of, say, 100 men, measure their heights, and calculate the sample mean. This calculated average can then serve as an estimate of the average height of all adult men in that city.The formula for calculating the sample mean is straightforward. It involves summing up all the values in the sample and then dividing by the number of observations in that sample. Mathematically, it can be expressed as:sample mean = (Σx) / nwhere Σx is the sum of all sample values, and n is the number of observations in the sample. This formula highlights how the sample mean serves as a central point around which the data is distributed.One of the key advantages of using the sample mean is that it allows researchers to make inferences about a population based on limited data. However, it is essential to note that the reliability of the sample mean depends significantly on how representative the sample is of the population. If the sample is biased or too small, the sample mean may not accurately reflect the true population mean.Moreover, when dealing with the sample mean, one must also consider the concept of variability. Different samples taken from the same population can yield different sample means. Therefore, statisticians often calculate the standard deviation or standard error of the sample mean to understand the degree of variation and uncertainty associated with their estimate.In conclusion, the sample mean is a foundational concept in statistics that plays a vital role in data analysis. It enables researchers to summarize and make informed decisions based on a smaller subset of data rather than the entire population. By understanding and accurately calculating the sample mean, researchers can derive meaningful insights that contribute to various fields such as psychology, economics, and public health. Thus, mastering the concept of sample mean is essential for anyone involved in data-driven research and analysis.

在统计学领域，理解样本均值的概念对于数据分析和解释至关重要。样本均值，在中文中称为“样本均值”，指的是从一个更大的总体中抽取的子集数据的平均值。这一指标提供了对总体特征的洞察，而无需分析每一个数据点。为了说明样本均值的重要性，让我们考虑一个实际的例子。假设一位研究者想要了解一个城市成年男性的平均身高。由于时间和资源的限制，测量城市中每个男性的身高是不切实际的。因此，研究者可以随机抽取100名男性，测量他们的身高，并计算出样本均值。这个计算出的平均值可以作为该城市所有成年男性平均身高的估计。计算样本均值的公式很简单。它涉及将样本中的所有值相加，然后除以样本中的观察数量。数学上，这可以表示为：样本均值 = (Σx) / n其中Σx是所有样本值的总和，n是样本中的观察数量。这个公式突出了样本均值作为数据分布中心点的作用。使用样本均值的一个主要优点是，它允许研究者基于有限的数据对总体进行推断。然而，需要注意的是，样本均值的可靠性在很大程度上取决于样本是否能代表总体。如果样本存在偏差或太小，样本均值可能无法准确反映真实的总体均值。此外，在处理样本均值时，还必须考虑变异性的概念。从同一总体中提取的不同样本可能会产生不同的样本均值。因此，统计学家通常计算样本均值的标准差或标准误，以了解与其估计相关的变异程度和不确定性。总之，样本均值是统计学中的基础概念，在数据分析中发挥着重要作用。它使研究者能够总结并基于较小的数据子集做出明智的决策，而不是依赖整个总体。通过理解和准确计算样本均值，研究者可以得出有意义的见解，这些见解有助于心理学、经济学和公共健康等各个领域。因此，掌握样本均值的概念对于任何参与数据驱动研究和分析的人来说都是至关重要的。