proportional sampling

简明释义

比例抽样

英英释义

例句

1.In a survey of customer satisfaction, we used proportional sampling to ensure that each demographic group was represented according to their size in the population.

在客户满意度调查中，我们使用了比例抽样，以确保每个年龄段的群体根据其在总人口中的比例得到代表。

2.By implementing proportional sampling, the study accurately reflected the voting preferences of various ethnic groups.

通过实施比例抽样，该研究准确反映了各个族群的投票偏好。

3.The research team applied proportional sampling to gather data from different regions, ensuring that larger populations were sampled more frequently.

研究小组应用了比例抽样，从不同地区收集数据，确保较大的人口被更频繁地抽样。

4.The school conducted a proportional sampling of students to assess the effectiveness of its new teaching methods.

学校对学生进行了比例抽样，以评估其新教学方法的有效性。

5.Using proportional sampling allowed the researchers to minimize bias and improve the reliability of their findings.

使用比例抽样使研究人员能够最小化偏差，提高研究结果的可靠性。

作文

In the field of research and statistics, sampling methods play a crucial role in ensuring that the data collected is representative of the larger population. One effective method for achieving this is through proportional sampling, which ensures that different subgroups within a population are represented in proportion to their size. This technique is particularly valuable when researchers aim to understand the characteristics or behaviors of specific segments of a population while minimizing bias.To illustrate the importance of proportional sampling, consider a study conducted to assess the dietary habits of high school students in a large city. The total population consists of students from various ethnic backgrounds, socioeconomic statuses, and academic performance levels. If the researcher simply selects a random sample of students without considering these factors, the results may be skewed. For example, if most of the sampled students happen to belong to a single ethnic group, the findings would not accurately reflect the dietary habits of the entire student population.By employing proportional sampling, the researcher can ensure that each subgroup—such as ethnicity, income level, and academic performance—is represented in the sample in accordance with its proportion in the overall population. This means that if 30% of the student population is Hispanic, then 30% of the sample should also be Hispanic. This careful selection process allows for a more accurate representation of the entire population, leading to more reliable and valid conclusions.Moreover, proportional sampling can enhance the quality of quantitative research. When researchers analyze data collected through this method, they can apply statistical techniques that assume the sample reflects the population accurately. This improves the generalizability of the findings, allowing researchers to make more confident recommendations based on their results.However, implementing proportional sampling is not without challenges. Researchers must first identify the relevant subgroups within the population and obtain accurate data about their sizes. This may require extensive preliminary research and access to databases that provide demographic information. Additionally, if the population is difficult to define or access, it may complicate the sampling process, potentially leading to errors in representation.Despite these challenges, the benefits of proportional sampling far outweigh the drawbacks. It fosters inclusivity and ensures that the voices of all segments of the population are heard. This is especially important in fields such as public health, education, and social sciences, where understanding diverse perspectives can lead to improved policies and programs.In conclusion, proportional sampling is an essential technique in research that enhances the representativeness of samples. By ensuring that different subgroups are included according to their actual proportions in the population, researchers can draw more accurate conclusions and make informed decisions. As society becomes increasingly diverse, the need for such inclusive research methodologies will only grow, making proportional sampling a vital tool for future studies.

在研究和统计领域，抽样方法在确保收集的数据能够代表更大群体方面发挥着至关重要的作用。一种有效的方法是通过比例抽样，该方法确保人口中不同子群体按其规模进行代表。当研究人员希望了解特定群体的特征或行为时，这种技术尤为宝贵，同时可以尽量减少偏差。为了说明比例抽样的重要性，可以考虑一项研究，该研究旨在评估一个大城市高中学生的饮食习惯。总人口由来自各种族背景、社会经济地位和学业表现水平的学生组成。如果研究人员简单地随机选择学生而不考虑这些因素，结果可能会出现偏差。例如，如果大多数被抽样学生恰好属于同一族裔，则研究结果将无法准确反映整个学生群体的饮食习惯。通过采用比例抽样，研究人员可以确保每个子群体（如种族、收入水平和学业表现）在样本中的比例与其在总体人口中的比例相符。这意味着如果30%的学生人口是西班牙裔，那么样本中也应该有30%是西班牙裔。这种细致的选择过程允许更准确地代表整个群体，从而得出更可靠和有效的结论。此外，比例抽样还可以提高定量研究的质量。当研究人员分析通过这种方法收集的数据时，他们可以应用假设样本准确反映人口的统计技术。这提高了研究结果的普遍适用性，使研究人员能够根据结果做出更有信心的建议。然而，实施比例抽样并非没有挑战。研究人员必须首先确定人口中相关的子群体，并获得有关其规模的准确数据。这可能需要广泛的初步研究和访问提供人口统计信息的数据库。此外，如果人口难以定义或接触，可能会使抽样过程复杂化，从而可能导致代表性的错误。尽管存在这些挑战，但比例抽样的好处远远超过缺点。它促进了包容性，并确保所有群体的声音都被听到。这在公共卫生、教育和社会科学等领域尤其重要，因为理解多样化的观点可以改善政策和项目。总之，比例抽样是研究中一种必不可少的技术，它增强了样本的代表性。通过确保不同子群体按照其在总体人口中的实际比例被纳入，研究人员可以得出更准确的结论并做出明智的决策。随着社会日益多元化，对这种包容性研究方法的需求只会增加，使得比例抽样成为未来研究的重要工具。