extensive sampling

简明释义

外延抽样

英英释义

例句

1.To improve product quality, the company implemented extensive sampling during the manufacturing process.

为了提高产品质量，公司在生产过程中实施了广泛的抽样。

2.The environmental study required extensive sampling across multiple sites to assess pollution levels accurately.

这项环境研究需要在多个地点进行广泛的抽样以准确评估污染水平。

3.The research team conducted extensive sampling to ensure the data was representative of the entire population.

研究团队进行了广泛的抽样以确保数据能够代表整个群体。

4.The survey used extensive sampling methods to gather opinions from various demographic groups.

该调查采用了广泛的抽样方法，从不同的人口统计群体中收集意见。

5.By utilizing extensive sampling, we were able to identify trends that would have otherwise gone unnoticed.

通过利用广泛的抽样，我们能够识别出本来会被忽视的趋势。

作文

In the field of research and data analysis, the concept of extensive sampling plays a crucial role in ensuring the reliability and validity of results. Extensive sampling refers to the process of collecting data from a large and diverse set of subjects or observations. This method is particularly important in studies where the goal is to understand complex phenomena or to make generalizations about a larger population. By gathering information from a wide array of sources, researchers can reduce biases that may arise from limited or selective sampling methods.For instance, consider a study aimed at understanding consumer behavior in the fast-food industry. If researchers only surveyed customers from one specific restaurant, their findings might not accurately reflect the preferences of the broader population. However, by employing extensive sampling, which involves surveying customers from multiple restaurants across various locations and demographics, the researchers can obtain a more comprehensive view of consumer habits.Moreover, extensive sampling allows for greater statistical power in analyses. When researchers collect data from a larger pool, they increase the likelihood of detecting significant effects or relationships that may not be apparent in smaller samples. This is particularly relevant in fields such as medicine, where understanding the efficacy of a treatment requires robust data from diverse patient groups. A clinical trial that utilizes extensive sampling can provide insights that are more applicable to the general population, thereby enhancing the credibility of the study's conclusions.In addition to improving the quality of data, extensive sampling also aids in identifying outliers and variations within the dataset. When researchers gather data from a wide range of subjects, they are better equipped to recognize patterns and anomalies that could influence their findings. For example, if a study on educational outcomes only included students from affluent neighborhoods, it might overlook critical factors affecting students from lower-income areas. By implementing extensive sampling, researchers can ensure that their analyses account for these variations, leading to more nuanced and actionable insights.Furthermore, the implications of extensive sampling extend beyond the immediate research context. The findings derived from studies that employ this method can inform policy decisions, marketing strategies, and even educational reforms. For instance, policymakers who base their decisions on research that utilized extensive sampling can be more confident that their initiatives will address the needs of a diverse population, ultimately leading to more effective outcomes.In conclusion, extensive sampling is an indispensable tool in research that enhances the reliability, validity, and applicability of findings. By collecting data from a broad and varied range of subjects, researchers can minimize biases, increase statistical power, and uncover valuable insights that would otherwise remain hidden. As the landscape of research continues to evolve, embracing extensive sampling will be essential for driving meaningful advancements across various fields. Thus, it is imperative for researchers to prioritize this methodology in their studies to ensure that their conclusions are robust and representative of the populations they aim to serve.

在研究和数据分析领域，广泛抽样的概念在确保结果的可靠性和有效性方面发挥着至关重要的作用。广泛抽样是指从大量多样的对象或观察中收集数据的过程。这种方法在研究的目标是理解复杂现象或对更大人群进行概括时尤为重要。通过从广泛的来源收集信息，研究人员可以减少有限或选择性抽样方法可能带来的偏见。例如，考虑一项旨在了解快餐行业消费者行为的研究。如果研究人员仅调查一家特定餐厅的顾客，他们的发现可能无法准确反映更广泛人群的偏好。然而，通过采用广泛抽样，即从多个餐厅、不同地点和人口统计中调查顾客，研究人员可以获得对消费者习惯的更全面的了解。此外，广泛抽样还提高了分析的统计效能。当研究人员从较大的样本池收集数据时，他们增加了检测显著效应或关系的可能性，这些效应或关系在较小样本中可能并不明显。这在医学等领域尤其相关，因为理解治疗的有效性需要来自多样患者群体的强大数据。利用广泛抽样的临床试验可以提供更适用于一般人群的见解，从而增强研究结论的可信度。除了提高数据质量，广泛抽样还有助于识别数据集中的异常值和变异。当研究人员从广泛的对象收集数据时，他们更能够识别可能影响其发现的模式和异常。例如，如果一项关于教育成果的研究只包括富裕社区的学生，它可能会忽视影响低收入地区学生的关键因素。通过实施广泛抽样，研究人员可以确保其分析考虑到这些变异，从而得出更细致和可行的见解。此外，广泛抽样的影响超出了即时的研究背景。基于采用此方法的研究得出的发现可以为政策决策、营销策略甚至教育改革提供信息。例如，基于利用广泛抽样的研究结果做出决策的政策制定者可以更有信心地认为他们的举措将满足多样人群的需求，最终导致更有效的结果。总之，广泛抽样是研究中不可或缺的工具，增强了发现的可靠性、有效性和适用性。通过从广泛且多样的对象中收集数据，研究人员可以最小化偏见、增加统计效能，并揭示否则会被隐藏的宝贵见解。随着研究领域的不断发展，拥抱广泛抽样对于推动各个领域的有意义进展至关重要。因此，研究人员必须在其研究中优先考虑这一方法，以确保其结论稳健且能代表他们旨在服务的人群。