statistical estimation

简明释义

统计估计

英英释义

例句

1.In our project, we relied on statistical estimation to assess the average income of households in the area.

在我们的项目中，我们依靠统计估计来评估该地区家庭的平均收入。

2.Using statistical estimation, the team was able to determine the effectiveness of the new drug.

通过使用统计估计，团队能够确定新药的有效性。

3.The researcher used statistical estimation to predict the population growth in the next decade.

研究人员使用统计估计来预测未来十年的人口增长。

4.The statistical estimation of the survey results showed a significant trend in consumer behavior.

调查结果的统计估计显示出消费者行为的显著趋势。

5.The statistical estimation provided by the analysts helped the company make informed decisions.

分析师提供的统计估计帮助公司做出明智的决策。

作文

In today's data-driven world, the concept of statistical estimation plays a crucial role in various fields, including economics, healthcare, and social sciences. Statistical estimation refers to the process of inferring the characteristics of a population based on a sample of data. This technique is essential for researchers and analysts who need to make decisions or predictions without having access to an entire population dataset. For instance, in public health, officials often rely on statistical estimation to determine the prevalence of a disease within a community by analyzing a smaller group of individuals. The importance of statistical estimation lies in its ability to provide insights while minimizing the resources required for data collection. Collecting data from an entire population can be time-consuming, costly, and impractical. By using statistical estimation, researchers can draw meaningful conclusions from a representative sample, allowing them to generalize their findings to the larger population. This approach not only saves time and money but also enables timely decision-making in critical situations.Moreover, statistical estimation involves various methods, including point estimation and interval estimation. Point estimation provides a single value as an estimate of a population parameter, while interval estimation offers a range of values within which the parameter is expected to fall. Both methods have their advantages and limitations, and the choice between them often depends on the specific requirements of the study. For example, if a researcher needs a quick estimate of a population mean, they might opt for point estimation. However, if they want to account for uncertainty and provide a more comprehensive view, interval estimation would be more appropriate.In addition to its practical applications, statistical estimation also raises important questions about accuracy and reliability. The quality of the estimates depends heavily on the sample size and the sampling method used. Larger sample sizes generally lead to more reliable estimates, as they reduce the margin of error. Furthermore, employing random sampling techniques helps ensure that the sample is representative of the population, thereby enhancing the validity of the statistical estimation.Nonetheless, it is crucial to recognize that statistical estimation is not without its challenges. Misinterpretation of results can occur if the assumptions underlying the estimation process are violated. For instance, if the sample is biased or not randomly selected, the estimates may not accurately reflect the population. Therefore, researchers must be diligent in their methodology and transparent about the limitations of their estimates.In conclusion, statistical estimation is an invaluable tool for making informed decisions based on limited data. Its applications span across various domains, providing insights that can influence policies, strategies, and practices. As we continue to navigate an increasingly complex world, the ability to effectively utilize statistical estimation will remain essential for researchers, policymakers, and business leaders alike. Understanding its principles and potential pitfalls is key to harnessing its power for better decision-making and improved outcomes in society.

在当今数据驱动的世界中，统计估计的概念在经济学、医疗保健和社会科学等多个领域发挥着至关重要的作用。统计估计是指基于一组数据样本推断总体特征的过程。这种技术对需要做出决策或预测但无法获取整个总体数据集的研究人员和分析师至关重要。例如，在公共卫生领域，官员们通常依赖于统计估计来通过分析较小的个体群体来确定某种疾病在社区中的流行程度。统计估计的重要性在于它能够在最小化数据收集所需资源的同时提供洞察力。从整个总体中收集数据可能耗时、成本高昂且不切实际。通过使用统计估计，研究人员可以从一个具有代表性的样本中得出有意义的结论，从而将他们的发现推广到更大的人群中。这种方法不仅节省了时间和金钱，还能够在关键情况下及时做出决策。此外，统计估计涉及多种方法，包括点估计和区间估计。点估计提供一个单一值作为总体参数的估计，而区间估计则提供一个值范围，其中该参数预计会落入。两种方法各有优缺点，选择其中一种通常取决于研究的具体要求。例如，如果研究人员需要对总体均值进行快速估计，他们可能会选择点估计。然而，如果他们想考虑不确定性并提供更全面的视角，则区间估计会更为合适。除了其实际应用外，统计估计还引发了关于准确性和可靠性的重要问题。估计的质量在很大程度上依赖于样本大小和所使用的抽样方法。较大的样本量通常会导致更可靠的估计，因为它们减少了误差范围。此外，采用随机抽样技术有助于确保样本具有代表性，从而增强统计估计的有效性。然而，必须认识到，统计估计并非没有挑战。如果违反了估计过程的假设，结果可能会被错误解读。例如，如果样本存在偏差或未随机选择，则估计可能无法准确反映总体。因此，研究人员必须在其方法论上保持谨慎，并对其估计的局限性保持透明。总之，统计估计是基于有限数据做出明智决策的宝贵工具。它的应用跨越多个领域，提供了可以影响政策、战略和实践的见解。随着我们继续在日益复杂的世界中导航，有效利用统计估计的能力将对研究人员、政策制定者和商业领袖而言仍然至关重要。理解其原则和潜在陷阱是利用其力量以实现更好的决策和改善社会结果的关键。