sampling arrangement

简明释义

取样装置

英英释义

例句

1.The sampling arrangement was crucial for the accuracy of the survey results.

这项抽样安排对调查结果的准确性至关重要。

2.The research team designed a new sampling arrangement to ensure diverse participant representation.

研究团队设计了一种新的抽样安排以确保参与者的多样性。

3.Due to budget constraints, we had to simplify our sampling arrangement significantly.

由于预算限制，我们不得不大幅简化我们的抽样安排。

4.A stratified sampling arrangement can help us target specific demographics more effectively.

分层抽样安排可以帮助我们更有效地针对特定的人口统计。

5.We need to finalize the sampling arrangement before we can start collecting data.

在开始收集数据之前，我们需要确定抽样安排。

作文

In the world of research and data analysis, a well-structured approach is crucial for obtaining reliable results. One of the fundamental concepts that researchers must understand is the sampling arrangement. A sampling arrangement refers to the method used to select a subset of individuals or items from a larger population to make inferences about that population. This concept is vital because the way in which samples are collected can significantly impact the validity and reliability of the research findings.To illustrate the importance of a sampling arrangement, consider a study aimed at understanding the dietary habits of teenagers in a large city. If the researchers simply choose to survey their friends or classmates, they might end up with a biased sample that does not accurately represent the broader population of teenagers in that city. Instead, a systematic sampling arrangement would involve using random selection methods to ensure that every teenager has an equal chance of being included in the sample. This could involve using school rosters, social media platforms, or other databases to randomly select participants.There are several types of sampling arrangements that researchers can employ, each with its own advantages and disadvantages. For instance, simple random sampling is one of the most straightforward methods, where each member of the population has an equal chance of being selected. However, this method may not always be practical, especially in large populations.On the other hand, stratified sampling involves dividing the population into subgroups (or strata) based on certain characteristics, such as age, gender, or socioeconomic status, and then randomly selecting samples from each subgroup. This approach ensures that the sample reflects the diversity of the population, thereby improving the accuracy of the results.Another common sampling arrangement is cluster sampling, where the population is divided into clusters, usually geographically, and entire clusters are randomly selected. This method can be more cost-effective and easier to implement than other sampling techniques, particularly when dealing with large populations spread over wide areas.The choice of a sampling arrangement also depends on the research objectives and the nature of the population being studied. For example, if a researcher is investigating a rare condition, they may opt for purposive sampling, where individuals are selected based on specific characteristics relevant to the study. This targeted approach can yield valuable insights but may limit the generalizability of the findings.Moreover, it is crucial for researchers to document their sampling arrangements clearly in their reports. Transparency in how samples were selected allows others to evaluate the credibility of the research and replicate the study if desired. This documentation should include details about the population, the sampling method used, and any potential biases that could affect the results.In conclusion, a sound sampling arrangement is a cornerstone of effective research. By carefully selecting how samples are chosen, researchers can enhance the validity of their findings and contribute valuable knowledge to their fields. Understanding the various types of sampling arrangements and their implications is essential for anyone involved in data collection and analysis. As the saying goes, 'garbage in, garbage out'; without a proper sampling arrangement, even the most sophisticated analysis can lead to misleading conclusions.

在研究和数据分析的世界中，结构良好的方法对于获得可靠的结果至关重要。研究人员必须理解的基本概念之一是抽样安排。抽样安排是指用于从更大人群中选择个体或项目子集的方法，以对该人群进行推断。这个概念至关重要，因为样本的收集方式会显著影响研究结果的有效性和可靠性。为了说明抽样安排的重要性，考虑一个旨在了解大城市青少年饮食习惯的研究。如果研究人员仅选择调查他们的朋友或同学，他们可能会得到一个偏倚的样本，无法准确代表该城市更广泛的青少年人群。相反，一个系统的抽样安排将涉及使用随机选择方法，以确保每个青少年都有同等机会被纳入样本。这可以通过使用学校名册、社交媒体平台或其他数据库来随机选择参与者。研究人员可以采用几种类型的抽样安排，每种都有其优缺点。例如，简单随机抽样是最简单的方法之一，其中每个成员都有同等的被选择机会。然而，这种方法在大人群中并不总是实用。另一方面，分层抽样涉及根据某些特征（如年龄、性别或社会经济地位）将人群划分为子组（或层），然后从每个子组中随机选择样本。这种方法确保样本反映了人群的多样性，从而提高了结果的准确性。另一种常见的抽样安排是集群抽样，其中人群被划分为集群，通常是按地理位置，然后随机选择整个集群。这种方法在处理覆盖广泛区域的大型人群时，可能比其他抽样技术更具成本效益和实施便利性。抽样安排的选择还取决于研究目标和被研究人群的性质。例如，如果研究人员正在调查一种罕见的疾病，他们可能会选择目的性抽样，即根据与研究相关的特定特征选择个体。这种有针对性的方法可以提供有价值的见解，但可能会限制研究结果的普遍适用性。此外，研究人员在报告中清楚记录他们的抽样安排也至关重要。对样本选择方式的透明度使他人能够评估研究的可信度，并在需要时复制该研究。这种文档应包括有关人群、所使用的抽样方法以及可能影响结果的任何潜在偏差的详细信息。总之，合理的抽样安排是有效研究的基石。通过仔细选择样本的选择方式，研究人员可以增强其发现的有效性，并为其领域贡献有价值的知识。理解各种类型的抽样安排及其影响对于任何参与数据收集和分析的人来说都是至关重要的。正如谚语所说，“垃圾进，垃圾出”；没有适当的抽样安排，即使是最复杂的分析也可能导致误导性的结论。