sampling without replacement

简明释义

无退还抽样

英英释义

例句

1.In our study, we conducted a survey using sampling without replacement to ensure that each participant was only selected once.

在我们的研究中，我们使用了不放回抽样进行调查，以确保每位参与者只被选中一次。

2.When selecting books for a reading program, the librarian used sampling without replacement to avoid duplicating any titles.

在为阅读项目选择书籍时，图书管理员使用了不放回抽样以避免重复任何书名。

3.The lottery system operates on the principle of sampling without replacement, meaning once a number is drawn, it cannot be drawn again.

彩票系统基于不放回抽样的原则，这意味着一旦一个号码被抽出，它就不能再被抽出。

4.In clinical trials, researchers often use sampling without replacement to select patients for different treatment groups.

在临床试验中，研究人员经常使用不放回抽样来选择不同治疗组的患者。

5.The game show featured a quiz format that involved sampling without replacement from a pool of questions.

这个游戏节目采用了一种测验格式，涉及从题库中进行不放回抽样。

作文

In the field of statistics, the concept of sampling without replacement plays a crucial role in various research methodologies. To understand this concept better, we need to delve into what it means and how it is applied in real-world scenarios. Sampling without replacement refers to the process of selecting items from a larger population in such a way that once an item is selected, it cannot be chosen again for that particular sample. This method contrasts with sampling with replacement, where selected items can be chosen multiple times. The distinction between these two sampling methods is essential for researchers when designing their studies.One of the primary advantages of sampling without replacement is that it ensures diversity within the sample. Since each item can only be selected once, the researcher can gather a broader range of data points from the population. This is particularly useful in surveys or experiments where the goal is to obtain a representative sample that captures the variability of the entire population. For example, if a researcher wants to study the eating habits of high school students across a city, using sampling without replacement allows them to select different students from various schools. This approach minimizes the risk of bias that could occur if the same students were selected multiple times.Moreover, sampling without replacement is often used in quality control processes in manufacturing. In these situations, inspectors may randomly select a limited number of products from a production batch to test for defects. By employing this method, they can ensure that each product is unique in the sample, which helps in accurately assessing the overall quality of the batch. If they were to use sampling with replacement, the same defective product could potentially be tested multiple times, leading to misleading conclusions about the quality of the entire batch.However, researchers must also be aware of the limitations associated with sampling without replacement. One significant challenge is that as items are removed from the population during the sampling process, the remaining pool of items becomes smaller. This reduction can affect the statistical properties of the sample, especially if the sample size is not adequately large compared to the population size. Consequently, researchers must carefully consider the sample size when implementing sampling without replacement to ensure that the results remain statistically valid and reliable.In conclusion, the concept of sampling without replacement is integral to many statistical analyses and research designs. It provides a method for obtaining diverse and representative samples while minimizing bias. However, researchers must also account for the potential limitations that come with reducing the population size during the sampling process. Overall, understanding and effectively applying sampling without replacement can significantly enhance the quality and reliability of research findings. As we continue to explore various statistical methods, the importance of sampling techniques like sampling without replacement will remain a fundamental aspect of conducting robust and credible research.

在统计学领域，不放回抽样的概念在各种研究方法中发挥着至关重要的作用。要更好地理解这一概念，我们需要深入探讨它的含义以及它在现实世界中的应用。不放回抽样是指从较大总体中选择项目的过程，在这种情况下，一旦选择了一个项目，它就不能在该特定样本中再次被选择。这种方法与有放回抽样形成对比，在有放回抽样中，所选项目可以多次被选择。这两种抽样方法之间的区别对于研究人员在设计研究时至关重要。不放回抽样的主要优点之一是它确保样本的多样性。由于每个项目只能被选择一次，研究人员可以从总体中收集更广泛的数据点。这在调查或实验中尤其有用，其目标是获得一个能够捕捉整个总体变异性的代表性样本。例如，如果研究人员想要研究一个城市高中生的饮食习惯，使用不放回抽样可以让他们从不同的学校中选择不同的学生。这种方法最小化了如果同一学生被多次选择可能出现的偏见风险。此外，不放回抽样通常用于制造业的质量控制过程中。在这些情况下，检查员可能会随机选择一批产品中的有限数量进行缺陷测试。通过采用这种方法，他们可以确保样本中的每个产品都是独特的，这有助于准确评估整批产品的整体质量。如果他们使用的是有放回抽样，则可能会多次测试同一缺陷产品，从而导致对整批产品质量的误导性结论。然而，研究人员也必须意识到与不放回抽样相关的局限性。一个显著的挑战是，随着抽样过程中项目的移除，剩余的项目池变得更小。这种减少可能影响样本的统计特性，特别是在样本量相对于总体规模不够大的情况下。因此，研究人员在实施不放回抽样时必须仔细考虑样本大小，以确保结果保持统计有效性和可靠性。总之，不放回抽样的概念是许多统计分析和研究设计中不可或缺的部分。它提供了一种获取多样且具有代表性的样本的方法，同时最小化偏见。然而，研究人员也必须考虑在抽样过程中总体规模减少所带来的潜在限制。总体而言，理解并有效应用不放回抽样可以显著提高研究结果的质量和可靠性。随着我们继续探索各种统计方法，像不放回抽样这样的抽样技术的重要性将始终是进行稳健和可信研究的基本方面。