sampling

简明释义

英[ˈsɑːmplɪŋ]美[ˈsæmplɪŋ]

n. 取样；抽样

v. 取样；抽样（sample 的 ing 形式）

英英释义

单词用法

同义词

反义词

例句

1.A method of systematic sampling has been adopted for Giant panda (Ailuropoda melanoleuca) in Foping Nature Reserve.

已对佛坪自然保护区的大熊猫采用系统采样的方法。

2.As with the bees, the broad sampling of options before a decision is made will usually result in a compromise acceptable to all.

就像蜂群一样，在做决定之前先进行广泛的抽样调查，通常能找到所有人都能接受的折中决策。

3.Time sampling means that researchers choose various time intervals for their observation.

时间采样是指研究人员选择不同的时间间隔进行观察。

4.These include hand collecting, using baits to attract the ants, ground litter sampling, and the use of pitfall traps.

这些包括人工收集，使用诱饵吸引蚂蚁，地面垃圾取样，以及使用陷阱诱捕。

5.Here's a sampling of the wackiest questions.

这里有一些非常古怪的问题样本。

6.Set the Sampling interval to: 5 minutes

设置Samplinginterval为：5分钟

7.The sampling frequency is specified in samples per day.

抽样频率用每天抽样次数指定。

8.What kind of sampling procedure do you use?

你将采用什么样的抽样程序？

9.The company used customer feedback sampling to improve their services.

公司使用客户反馈的抽样来改善他们的服务。

10.During the event, we will be sampling different types of food from local vendors.

在活动期间，我们将会对当地供应商提供的不同类型的食物进行抽样。

11.The sampling method chosen by the scientists was stratified to ensure diverse representation.

科学家选择的抽样方法是分层的，以确保多样化的代表性。

12.In quality control, sampling is essential to ensure products meet standards.

在质量控制中，抽样对于确保产品符合标准至关重要。

13.The researchers conducted a random sampling to gather data for their study.

研究人员进行了随机抽样以收集他们研究的数据。

作文

In the field of statistics, sampling refers to the process of selecting a subset of individuals from a larger population to estimate characteristics of the whole group. This technique is crucial in research, as it allows scientists and analysts to draw conclusions without needing to examine every single member of the population. For instance, if a researcher wants to understand the eating habits of teenagers in a large city, it would be impractical to survey every teenager. Instead, they can use sampling to select a representative group of teenagers, which can provide valuable insights into the broader population's behaviors. The effectiveness of sampling depends heavily on how well the sample represents the population. There are various methods of sampling, including random sampling, stratified sampling, and cluster sampling, each with its own advantages and disadvantages.Random sampling involves selecting individuals randomly from the population, ensuring that every member has an equal chance of being chosen. This method minimizes bias and allows for more generalizable results. However, it can still lead to unrepresentative samples if the population is heterogeneous. Stratified sampling, on the other hand, divides the population into subgroups or strata, such as age or income level, and then randomly samples from each stratum. This approach ensures that specific segments of the population are represented in the sample, which can enhance the accuracy of the results.Another method is cluster sampling, where the population is divided into clusters, usually based on geographical areas. A few clusters are randomly selected, and all individuals within those clusters are surveyed. While this method can be more cost-effective and easier to manage, it may introduce higher variability and potential bias if the selected clusters do not accurately represent the overall population.The choice of sampling method can significantly impact the validity of the research findings. Researchers must carefully consider their objectives, the nature of the population, and available resources when deciding which sampling technique to employ. Additionally, it is important to calculate the sample size appropriately to ensure that the results are statistically significant. A sample that is too small may not capture the diversity of the population, while an excessively large sample could waste resources and time.In conclusion, sampling is a fundamental concept in research and statistics that enables researchers to make informed decisions based on a smaller, manageable subset of data. By understanding the different sampling methods and their implications, researchers can design studies that yield reliable and valid results. As society continues to grow and evolve, the importance of effective sampling techniques will remain a cornerstone of empirical research, allowing us to understand complex issues and make data-driven decisions. Ultimately, mastering the art of sampling can lead to more accurate insights and a better understanding of the world around us.

在统计学领域，sampling 指的是从一个更大的人群中选择一个子集的过程，以估计整个群体的特征。这一技术在研究中至关重要，因为它允许科学家和分析师在不需要检查每一个成员的情况下得出结论。例如，如果一位研究者想要了解一个大城市青少年的饮食习惯，调查每个青少年将是不切实际的。相反，他们可以使用 sampling 从中选择一个具有代表性的青少年群体，这可以提供对更广泛人群行为的宝贵见解。sampling 的有效性在很大程度上取决于样本如何代表总体。sampling 有多种方法，包括随机抽样、分层抽样和聚类抽样，每种方法都有其优缺点。随机抽样涉及从总体中随机选择个体，确保每个成员都有平等的被选机会。这种方法最小化了偏差，并允许结果更具普遍性。然而，如果总体是异质的，这仍可能导致不具代表性的样本。另一方面，分层抽样将总体划分为子组或层，如年龄或收入水平，然后从每个层中随机抽样。这种方法确保特定人群段在样本中得到代表，这可以增强结果的准确性。另一种方法是聚类抽样，其中总体根据地理区域划分为集群。随机选择一些集群，并对这些集群中的所有个体进行调查。虽然这种方法可能更具成本效益且更易于管理，但如果所选集群不能准确代表整体人群，则可能引入更高的变异性和潜在偏差。sampling 方法的选择会显著影响研究结果的有效性。研究人员在决定采用哪种 sampling 技术时，必须仔细考虑他们的目标、总体的性质和可用资源。此外，适当地计算样本大小以确保结果具有统计显著性也很重要。样本过小可能无法捕捉到人群的多样性，而样本过大则可能浪费资源和时间。总之，sampling 是研究和统计中的一个基本概念，使研究人员能够基于较小、可管理的数据子集做出明智的决策。通过理解不同的 sampling 方法及其影响，研究人员可以设计出产生可靠和有效结果的研究。随着社会的不断发展，有效 sampling 技术的重要性将始终是实证研究的基石，使我们能够理解复杂问题并做出基于数据的决策。最终，掌握 sampling 的艺术可以带来更准确的见解，并更好地理解我们周围的世界。