sample standard deviation

简明释义

试样标准离差

英英释义

例句

1.To analyze the test scores of the students, we calculated the sample standard deviation 样本标准差 to understand the variability in their performance.

为了分析学生的考试成绩，我们计算了样本标准差 sample standard deviation以了解他们表现的变化性。

2.The sample standard deviation 样本标准差 of the sales data helps us determine how consistent our sales figures are over the last quarter.

销售数据的样本标准差 sample standard deviation帮助我们确定过去一个季度销售数字的一致性。

3.In a research study, the sample standard deviation 样本标准差 was used to measure the spread of responses among participants.

在一项研究中，使用样本标准差 sample standard deviation来衡量参与者之间反应的分布。

4.The sample standard deviation 样本标准差 is crucial for understanding the reliability of our experimental results.

对于理解我们实验结果的可靠性，样本标准差 sample standard deviation至关重要。

5.When comparing two groups, we need to calculate the sample standard deviation 样本标准差 for each group to assess the differences accurately.

在比较两个组时，我们需要计算每个组的样本标准差 sample standard deviation以准确评估差异。

作文

In the field of statistics, understanding the concept of sample standard deviation is crucial for analyzing data effectively. The sample standard deviation is a measure that quantifies the amount of variation or dispersion in a set of values. When researchers collect data from a sample rather than an entire population, they use the sample standard deviation to estimate how much the individual data points differ from the sample mean. This statistical tool is essential for making inferences about a larger population based on a smaller subset of data.To illustrate the importance of the sample standard deviation, consider a scenario where a teacher wants to understand the performance of her students in a mathematics test. Instead of grading every student in the school, she decides to take a sample of 30 students. After collecting their scores, she calculates the average score and finds that it is 75 out of 100. However, to fully understand the performance of her sample, she needs to calculate the sample standard deviation.By calculating the sample standard deviation, she can determine how spread out the students' scores are around the average. If the sample standard deviation is low, it indicates that most students scored close to the average, suggesting consistent performance. Conversely, a high sample standard deviation would imply that there is a significant variation in student scores, indicating that some students performed exceptionally well while others struggled.The formula for calculating the sample standard deviation involves several steps. First, you need to find the mean (average) of the sample data. Then, subtract the mean from each data point and square the result. Next, sum all these squared differences. Finally, divide this total by the number of observations minus one (this is known as Bessel's correction), and take the square root of the result. This process ensures that the sample standard deviation provides an unbiased estimate of the population standard deviation.Understanding how to compute and interpret the sample standard deviation is vital for students pursuing studies in fields such as psychology, economics, and health sciences, where data analysis is a key component. For example, in psychological research, a psychologist might conduct a study on anxiety levels among college students. By taking a sample of students and measuring their anxiety scores, they can calculate the sample standard deviation to understand how varied the anxiety levels are within that group. This information can be valuable for developing targeted interventions to help students manage their anxiety more effectively.In conclusion, the sample standard deviation is an essential statistical tool that helps researchers and analysts understand the variability within a sample of data. By grasping the concept and calculation of the sample standard deviation, individuals can make informed decisions based on their findings. Whether in education, psychology, or any other field, the ability to analyze data through the lens of the sample standard deviation ultimately enhances our comprehension of complex issues and aids in effective problem-solving.

在统计学领域，理解样本标准差的概念对于有效分析数据至关重要。样本标准差是一个量化一组数值变化或离散程度的度量。当研究人员从样本而不是整个总体中收集数据时，他们使用样本标准差来估计个别数据点与样本均值的差异。这种统计工具对于根据较小的数据子集推断更大的人口是必不可少的。为了说明样本标准差的重要性，考虑一个场景：一位教师想了解她的学生在数学测试中的表现。她决定抽取30名学生作为样本，而不是评估学校里的每位学生。在收集到他们的分数后，她计算出平均分为75分（满分100）。然而，为了全面了解她的样本，她需要计算样本标准差。通过计算样本标准差，她可以确定学生的分数围绕平均值的分散程度。如果样本标准差较低，表明大多数学生的分数接近平均值，提示表现一致。相反，如果样本标准差较高，则意味着学生的分数差异显著，暗示有些学生表现特别好，而其他学生则表现不佳。计算样本标准差的公式涉及几个步骤。首先，需要找到样本数据的均值（平均值）。然后，从每个数据点中减去均值，并将结果平方。接下来，将所有这些平方差求和。最后，将这个总和除以观察值的数量减去一（这被称为贝塞尔修正），并对结果开平方根。这个过程确保了样本标准差提供了对总体标准差的无偏估计。理解如何计算和解释样本标准差对于追求心理学、经济学和健康科学等领域学习的学生来说至关重要，因为数据分析是关键组成部分。例如，在心理学研究中，心理学家可能会对大学生的焦虑水平进行研究。通过对学生进行抽样并测量他们的焦虑评分，他们可以计算出样本标准差，以了解该组内焦虑水平的变化情况。这些信息对于制定针对性的干预措施以帮助学生更有效地管理焦虑非常有价值。总之，样本标准差是一个重要的统计工具，帮助研究人员和分析师理解样本数据中的变异性。通过掌握样本标准差的概念和计算方法，个人可以基于他们的发现做出明智的决策。无论是在教育、心理学还是其他任何领域，通过样本标准差的视角分析数据的能力最终增强了我们对复杂问题的理解，并有助于有效解决问题。