sampling with probability proportionate to size

简明释义

按概率比例抽样

英英释义

例句

1.During the ecological study, scientists employed sampling with probability proportionate to size 按规模比例抽样 to sample larger habitats more frequently.

在生态研究中，科学家采用了按规模比例抽样 sampling with probability proportionate to size以更频繁地抽样较大的栖息地。

2.The company applied sampling with probability proportionate to size 按规模比例抽样 to select stores for a customer satisfaction study based on their sales volume.

该公司应用了按规模比例抽样 sampling with probability proportionate to size根据销售额选择商店进行客户满意度研究。

3.The research team decided on sampling with probability proportionate to size 按规模比例抽样 to focus on urban areas with higher populations.

研究团队决定使用按规模比例抽样 sampling with probability proportionate to size以关注人口较多的城市地区。

4.For the national health assessment, sampling with probability proportionate to size 按规模比例抽样 was used to ensure diverse demographic representation.

在国家健康评估中，使用了按规模比例抽样 sampling with probability proportionate to size以确保多样的人口代表性。

5.In a large population survey, researchers used sampling with probability proportionate to size 按规模比例抽样 to ensure that larger communities were adequately represented.

在一项大规模人口调查中，研究人员使用了按规模比例抽样 sampling with probability proportionate to size以确保较大社区得到充分代表。

作文

In the field of statistics and research methodology, one important concept is sampling with probability proportionate to size. This sampling technique is particularly useful when researchers want to ensure that larger units have a higher chance of being selected than smaller units. This method is often employed in various fields such as market research, public health studies, and social science research, where the size of the population or the characteristics of the units being studied can significantly influence the results. To better understand this concept, let’s consider an example. Imagine a researcher is conducting a survey on the purchasing behavior of consumers in a city. The city is divided into several neighborhoods, each varying in population size. If the researcher uses simple random sampling, every neighborhood has an equal chance of being selected, regardless of how many people live there. However, this could lead to a situation where a small neighborhood is chosen, resulting in a sample that does not accurately reflect the overall consumer behavior of the city.By using sampling with probability proportionate to size, the researcher can ensure that neighborhoods with larger populations have a greater likelihood of being included in the sample. This approach increases the representativeness of the sample and enhances the reliability of the findings. For instance, if a neighborhood has a population of 10,000 people, while another has only 1,000, the former would be ten times more likely to be selected. This way, the final sample will more accurately mirror the diversity and behaviors of the entire city’s population.Moreover, sampling with probability proportionate to size can help in resource allocation. When researchers are aware that certain segments of the population are larger, they can allocate their resources more effectively, ensuring that they gather enough data from these larger segments. This can lead to more robust analyses and conclusions, ultimately benefiting the stakeholders involved in the research.In addition to its practical applications, it is essential to recognize the theoretical underpinnings of sampling with probability proportionate to size. This method is rooted in the principles of probability theory, which allows researchers to make inferences about a population based on a sample. By adhering to these principles, researchers can minimize bias and enhance the validity of their studies. Furthermore, the use of this sampling technique aligns with ethical research practices, as it ensures that all segments of the population are adequately represented.In conclusion, sampling with probability proportionate to size is a powerful tool in the arsenal of researchers. Its ability to enhance representativeness, improve resource allocation, and uphold ethical standards makes it an invaluable technique in statistical research. As the field continues to evolve, understanding and applying this method will remain crucial for researchers aiming to produce reliable and meaningful results. The implications of this sampling technique extend beyond academic research; it influences policy-making, business strategies, and public health initiatives, ultimately impacting society at large.

在统计学和研究方法论领域，一个重要的概念是按规模比例抽样。这种抽样技术在研究人员希望确保较大的单位比较小的单位有更高的被选中机会时特别有用。这种方法通常应用于市场研究、公共健康研究和社会科学研究等多个领域，在这些领域中，人口的大小或所研究单位的特征可能对结果产生重大影响。为了更好地理解这个概念，让我们考虑一个例子。假设一位研究人员正在进行一项关于城市消费者购买行为的调查。城市被划分为几个社区，每个社区的人口大小各不相同。如果研究人员使用简单随机抽样，每个社区都有平等的机会被选中，而不考虑居民人数。然而，这可能导致一个小社区被选择，从而导致样本无法准确反映整个城市的消费者行为。通过使用按规模比例抽样，研究人员可以确保人口较多的社区在样本中被选择的可能性更大。这种方法提高了样本的代表性，并增强了研究结果的可靠性。例如，如果一个社区有10,000人，而另一个只有1,000人，那么前者被选中的可能性将是后者的十倍。这样，最终样本将更准确地反映整个城市人口的多样性和行为。此外，按规模比例抽样还可以帮助资源分配。当研究人员意识到某些人口段较大时，他们可以更有效地分配资源，确保从这些较大段收集足够的数据。这可以导致更稳健的分析和结论，最终使研究相关的利益相关者受益。除了其实际应用外，认识到按规模比例抽样的理论基础也至关重要。这种方法根植于概率理论的原则，使研究人员能够根据样本对总体进行推断。遵循这些原则，研究人员可以最大限度地减少偏差，提高研究的有效性。此外，使用这种抽样技术符合伦理研究实践，因为它确保了所有人口段都得到充分代表。总之，按规模比例抽样是研究人员工具箱中的一种强大工具。它增强代表性、改善资源分配并维护伦理标准的能力，使其成为统计研究中不可或缺的技术。随着该领域的不断发展，理解和应用这一方法将对旨在产生可靠和有意义结果的研究人员至关重要。这种抽样技术的影响超越了学术研究；它影响政策制定、商业战略和公共卫生倡议，最终影响整个社会。