random sampling

简明释义

随机取样

英英释义

例句

1.In order to obtain accurate data, the survey was conducted using random sampling methods.

为了获得准确的数据，调查采用了随机抽样的方法。

2.By implementing random sampling, the scientists could eliminate bias in their research.

通过实施随机抽样，科学家们能够消除研究中的偏见。

3.The election commission employed random sampling to select voters for the exit polls.

选举委员会采用随机抽样来选择投票者进行出口民调。

4.The researchers used random sampling to ensure that every participant had an equal chance of being selected for the study.

研究人员使用随机抽样来确保每个参与者都有相等的机会被选中进行研究。

5.The quality control team utilized random sampling to test products from the production line.

质量控制团队利用随机抽样对生产线上的产品进行测试。

作文

In the field of statistics, random sampling is a fundamental technique used to gather data that is representative of a larger population. The essence of random sampling lies in its ability to select individuals from a population in such a way that every member has an equal chance of being chosen. This method is crucial because it minimizes bias and ensures that the results of a study can be generalized to the entire population. To illustrate the importance of random sampling, consider a scenario where a researcher aims to understand the eating habits of high school students in a large city. If the researcher were to only survey students from one affluent neighborhood, the findings could be skewed and not accurately reflect the diverse eating habits across different socioeconomic backgrounds. However, by employing random sampling, the researcher can select students from various neighborhoods, thus obtaining a more comprehensive view of the population’s eating habits.There are several methods of random sampling, including simple random sampling, stratified sampling, and systematic sampling. Simple random sampling involves selecting individuals entirely by chance, ensuring that each member of the population has an equal opportunity to be included. Stratified sampling, on the other hand, divides the population into distinct subgroups or strata and then randomly samples from each stratum. This technique is beneficial when researchers want to ensure representation from specific segments of the population. Lastly, systematic sampling involves selecting every nth individual from a list of the population, which can also yield a representative sample if done correctly.The advantages of random sampling are numerous. It enhances the reliability of research findings and allows for the use of statistical inference, which helps in making predictions about the population based on the sample data. Moreover, random sampling provides a foundation for various statistical tests and analyses, making it an indispensable tool in both academic research and practical applications.However, random sampling is not without its challenges. One significant issue is that achieving a truly random sample can be difficult, especially in populations that are not easily accessible or where there is a high level of non-response. Additionally, even with random sampling, there is still a possibility of sampling error, which refers to the difference between the sample result and the actual population parameter. Researchers must be mindful of these limitations and take steps to mitigate them, such as increasing sample size or using weighting techniques to adjust for any biases.In conclusion, random sampling is a vital technique in the realm of statistics that allows researchers to collect data in a manner that is unbiased and representative of a larger population. By understanding and implementing random sampling effectively, researchers can draw meaningful conclusions and contribute valuable insights into their fields of study. As we continue to navigate an increasingly data-driven world, the principles of random sampling will remain essential for ensuring the accuracy and reliability of research outcomes.

在统计学领域，随机抽样是一种基本技术，用于收集具有代表性的更大群体的数据。随机抽样的本质在于能够以这样的方式从一个群体中选择个体，使每个成员都有相等的被选中机会。这种方法至关重要，因为它最小化了偏见，并确保研究结果可以推广到整个群体。为了说明随机抽样的重要性，考虑一个场景：一位研究者希望了解一座大城市高中生的饮食习惯。如果研究者仅调查一个富裕社区的学生，结果可能会偏向，并不能准确反映不同社会经济背景下的多样化饮食习惯。然而，通过采用随机抽样，研究者可以从各个社区选择学生，从而获得更全面的群体饮食习惯视图。随机抽样有几种方法，包括简单随机抽样、分层抽样和系统抽样。简单随机抽样完全依靠偶然选择个体，确保每个群体成员都有平等的机会被包括在内。另一方面，分层抽样将群体划分为不同的子组或层，然后从每个层中随机抽样。当研究者希望确保特定群体的代表性时，这种技术非常有用。最后，系统抽样涉及从群体名单中选择每第n个个体，如果正确执行，也能产生具有代表性的样本。随机抽样的优点是众多的。它增强了研究结果的可靠性，并允许使用统计推断，这有助于根据样本数据对群体进行预测。此外，随机抽样为各种统计测试和分析提供了基础，使其成为学术研究和实际应用中不可或缺的工具。然而，随机抽样并非没有挑战。一个重大问题是实现真正的随机样本可能很困难，特别是在不易接触的群体中或存在高非响应率的情况下。此外，即使有随机抽样，仍然存在抽样误差的可能性，即样本结果与实际群体参数之间的差异。研究者必须注意这些局限性，并采取措施减轻这些问题，例如增加样本量或使用加权技术来调整任何偏见。总之，随机抽样是统计学领域的一项重要技术，使研究者能够以无偏和具有代表性的方式收集数据。通过有效理解和实施随机抽样，研究者可以得出有意义的结论，并为他们的研究领域贡献有价值的见解。随着我们继续在一个日益数据驱动的世界中航行，随机抽样的原则将始终是确保研究结果准确性和可靠性的关键。