sampling system

简明释义

取样系统

英英释义

例句

1.In clinical trials, a robust sampling system 采样系统 is crucial for obtaining reliable results.

在临床试验中，一个强大的采样系统 sampling system对获得可靠结果至关重要。

2.The environmental agency uses a sampling system 采样系统 to monitor air quality in urban areas.

环境机构使用采样系统 sampling system来监测城市地区的空气质量。

3.The new sampling system 采样系统 allows researchers to collect data more efficiently.

新的采样系统 sampling system使研究人员能够更高效地收集数据。

4.Our team implemented a sampling system 采样系统 to improve the quality of our product testing.

我们的团队实施了一个采样系统 sampling system以提高产品测试的质量。

5.We are developing a new sampling system 采样系统 for soil analysis to enhance agricultural practices.

我们正在开发一个新的采样系统 sampling system用于土壤分析，以增强农业实践。

作文

In today's data-driven world, the importance of an effective sampling system cannot be overstated. A sampling system refers to a method used to select a subset of individuals or observations from a larger population, allowing researchers and analysts to make inferences about the whole group without needing to examine every single member. This approach is particularly valuable in fields such as statistics, market research, and quality control. By employing a well-designed sampling system, organizations can save time and resources while still obtaining reliable and valid results.One of the key advantages of using a sampling system is that it enables researchers to gather data more efficiently. For instance, in market research, companies often cannot survey every potential customer due to time and budget constraints. Instead, they utilize a sampling system to select a representative group of individuals who can provide insights into consumer behavior and preferences. This not only speeds up the research process but also helps in making informed decisions based on the feedback received from the sample.Moreover, a well-implemented sampling system enhances the accuracy of the results. When the sample is carefully chosen to reflect the diversity of the entire population, the findings can be generalized with greater confidence. For example, if a pharmaceutical company is testing a new drug, they will use a sampling system to include participants from various demographics, ensuring that the results are applicable to different segments of the population. This careful selection process minimizes bias and increases the credibility of the research outcomes.However, it is crucial to note that not all sampling systems are created equal. There are various methods of sampling, including random sampling, stratified sampling, and cluster sampling, each with its own strengths and weaknesses. Random sampling, for instance, is often considered the gold standard because it gives every member of the population an equal chance of being selected. On the other hand, stratified sampling involves dividing the population into subgroups and then randomly selecting samples from each subgroup, which can be beneficial when certain characteristics are known to influence the outcome.The choice of a sampling system should align with the objectives of the research and the nature of the population being studied. Researchers must carefully consider factors such as sample size, variability within the population, and the potential for sampling error. A poorly designed sampling system can lead to inaccurate conclusions, which may have significant implications, particularly in fields like healthcare and public policy.In conclusion, a robust sampling system is essential for effective data collection and analysis. It allows researchers to draw meaningful conclusions while conserving resources and time. As we continue to navigate an increasingly complex world filled with vast amounts of data, mastering the art of sampling will be crucial for anyone involved in research or analytics. By understanding and applying the principles of a sampling system, we can ensure that our findings are both reliable and relevant, ultimately leading to better decision-making and outcomes across various sectors.

在当今数据驱动的世界中，有效的抽样系统的重要性不容忽视。抽样系统指的是一种从较大人群中选择个体或观察结果的方式，使研究人员和分析师能够对整个群体进行推断，而无需检查每一个成员。这种方法在统计学、市场研究和质量控制等领域尤为重要。通过采用精心设计的抽样系统，组织可以节省时间和资源，同时获得可靠有效的结果。使用抽样系统的一个主要优势是它使研究人员能够更高效地收集数据。例如，在市场研究中，由于时间和预算限制，公司通常无法调查每一个潜在客户。因此，他们利用抽样系统选择一组具有代表性的个体，以便提供有关消费者行为和偏好的见解。这不仅加快了研究过程，还帮助根据样本反馈做出明智的决策。此外，良好实施的抽样系统提高了结果的准确性。当样本经过仔细选择，能够反映整个群体的多样性时，研究结果可以更有信心地推广。例如，如果一家制药公司正在测试一种新药，他们将使用抽样系统包括来自不同人口统计的参与者，确保结果适用于不同的人群。这种仔细选择的过程最小化了偏差，增加了研究结果的可信度。然而，必须注意，并非所有的抽样系统都是一样的。抽样方法有多种，包括随机抽样、分层抽样和聚类抽样，每种都有其优缺点。随机抽样被认为是金标准，因为它给予每个成员相同的被选机会。另一方面，分层抽样涉及将人群划分为子组，然后从每个子组中随机选择样本，这在某些特征已知会影响结果时是有益的。选择抽样系统应与研究目标和所研究人群的性质相一致。研究人员必须仔细考虑样本大小、人群内的变异性以及抽样误差的潜在性。设计不当的抽样系统可能导致不准确的结论，这在医疗保健和公共政策等领域可能产生重大影响。总之，强大的抽样系统对于有效的数据收集和分析至关重要。它使研究人员能够得出有意义的结论，同时节省资源和时间。随着我们继续在一个充满大量数据的日益复杂的世界中导航，掌握抽样的艺术对于任何参与研究或分析的人来说都至关重要。通过理解和应用抽样系统的原则，我们可以确保我们的发现既可靠又相关，最终在各个领域实现更好的决策和结果。