stochastic sampling

简明释义

随机抽样

英英释义

例句

1.The team used stochastic sampling 随机抽样 to select a diverse group of participants for their clinical trial.

团队使用 随机抽样 stochastic sampling 来选择多样化的参与者进行临床试验。

2.In financial modeling, stochastic sampling 随机抽样 is used to simulate various market conditions and their impacts on investments.

在金融建模中，随机抽样 stochastic sampling 被用来模拟各种市场条件及其对投资的影响。

3.In machine learning, stochastic sampling 随机抽样 is often used to create training datasets from larger populations.

在机器学习中，随机抽样 stochastic sampling 常用于从更大的人群中创建训练数据集。

4.Researchers applied stochastic sampling 随机抽样 methods to estimate the population size of endangered species in a national park.

研究人员应用了 随机抽样 stochastic sampling 方法来估计国家公园内濒危物种的种群数量。

5.The stochastic sampling 随机抽样 technique helps in reducing bias in survey results by randomly selecting participants.

通过随机选择参与者，随机抽样 stochastic sampling 技术有助于减少调查结果中的偏差。

作文

Stochastic sampling is a method used in statistics and data analysis that involves selecting samples in a way that incorporates randomness. This technique is essential for ensuring that the sample accurately represents the population being studied, especially when it is impractical or impossible to collect data from every individual in that population. By using stochastic sampling (随机抽样), researchers can make inferences about the entire population based on the characteristics observed in the sample. One of the primary advantages of stochastic sampling (随机抽样) is that it helps to mitigate bias. When samples are chosen randomly, each member of the population has an equal chance of being selected. This randomness reduces the likelihood that the sample will be skewed by factors such as researcher bias or self-selection, which can occur in non-random sampling methods. For example, if a researcher only surveys individuals who volunteer to participate, the results may not accurately reflect the views of the broader population. In contrast, stochastic sampling (随机抽样) allows for a more balanced and representative collection of data.Another important aspect of stochastic sampling (随机抽样) is its application in various fields, including social sciences, healthcare, and market research. In social sciences, researchers often use stochastic sampling (随机抽样) to study public opinion on political issues. By randomly selecting participants from different demographics, they can gain insights into how various groups feel about certain topics. Similarly, in healthcare research, stochastic sampling (随机抽样) is crucial for clinical trials, where it is vital to ensure that the sample of patients reflects the diversity of the population affected by a particular disease. This approach helps to improve the validity of the findings and ensures that the results can be generalized to a larger audience.Moreover, stochastic sampling (随机抽样) is also used in the field of machine learning and artificial intelligence. In these areas, algorithms often rely on large datasets to learn patterns and make predictions. Using stochastic sampling (随机抽样) techniques can help in creating training datasets that are both diverse and representative, leading to models that perform better in real-world applications. For instance, when developing a recommendation system, employing stochastic sampling (随机抽样) can ensure that the algorithm is exposed to a wide range of user preferences, enhancing its ability to suggest relevant items.In conclusion, stochastic sampling (随机抽样) is a powerful tool in the world of statistics and data analysis. Its ability to reduce bias, enhance representativeness, and improve the validity of research findings makes it an invaluable method across various disciplines. As researchers continue to seek accurate and reliable data, the importance of stochastic sampling (随机抽样) will only grow, paving the way for more informed decision-making and better understanding of complex phenomena. By embracing this technique, we can ensure that our conclusions are grounded in solid evidence, ultimately leading to advancements in knowledge and practice.

随机抽样是一种在统计学和数据分析中使用的方法，它涉及以一种包含随机性的方式选择样本。这种技术对于确保样本准确代表所研究的人群至关重要，特别是在收集每个个体的数据不切实际或不可能时。通过使用随机抽样，研究人员可以根据样本中观察到的特征对整个群体进行推断。随机抽样的主要优点之一是它有助于减轻偏见。当样本是随机选择时，群体中的每个成员都有相等的被选中机会。这种随机性减少了样本因研究者偏见或自我选择等因素而失真的可能性，这在非随机抽样方法中常常发生。例如，如果研究者仅调查自愿参与的人，结果可能无法准确反映更广泛人群的观点。相反，随机抽样允许更平衡和具有代表性的数据显示。随机抽样的另一个重要方面是它在各个领域中的应用，包括社会科学、医疗保健和市场研究。在社会科学中，研究人员通常使用随机抽样来研究公众对政治问题的看法。通过从不同人口统计中随机选择参与者，他们可以深入了解各种群体对某些话题的感受。同样，在医疗研究中，随机抽样对于临床试验至关重要，在这里，确保患者样本反映出受特定疾病影响的人群的多样性是至关重要的。这种方法有助于提高研究结果的有效性，并确保结果可以推广到更大的人群。此外，随机抽样还用于机器学习和人工智能领域。在这些领域中，算法通常依赖于大型数据集来学习模式和做出预测。使用随机抽样技术可以帮助创建既多样又具有代表性的训练数据集，从而使模型在实际应用中表现得更好。例如，在开发推荐系统时，采用随机抽样可以确保算法接触到广泛的用户偏好，提高其建议相关项目的能力。总之，随机抽样是统计学和数据分析世界中的一种强大工具。它减少偏见、增强代表性和提高研究结果有效性的能力，使其在各个学科中成为不可或缺的方法。随着研究人员继续寻求准确和可靠的数据，随机抽样的重要性将只会增加，为更明智的决策和对复杂现象的更好理解铺平道路。通过采用这一技术，我们可以确保我们的结论建立在坚实的证据基础上，最终推动知识和实践的进步。