average sampling

简明释义

输入货物抽样检查

英英释义

例句

1.The researchers emphasized the importance of average sampling 平均抽样 in their methodology section.

研究人员在他们的方法论部分强调了<平均抽样>的重要性。

2.We used average sampling 平均抽样 to reduce bias in our survey results.

我们使用<平均抽样>来减少调查结果中的偏差。

3.The average sampling 平均抽样 method helped us gather data from different demographics.

这种<平均抽样>方法帮助我们从不同的人口统计中收集数据。

4.By applying average sampling 平均抽样, we were able to analyze trends over time.

通过应用<平均抽样>，我们能够分析随时间变化的趋势。

5.In our study, we conducted an average sampling 平均抽样 of the population to ensure accurate results.

在我们的研究中，我们进行了一个<平均抽样>以确保结果的准确性。

作文

In statistical analysis, the concept of average sampling plays a crucial role in understanding data sets and making informed decisions. Average sampling refers to the process of selecting a subset of individuals or items from a larger population to estimate the characteristics or behaviors of that entire population. This method is essential for researchers, businesses, and policymakers as it allows them to draw conclusions without having to analyze every single member of the group, which can be time-consuming and costly.The importance of average sampling lies in its ability to provide a representative snapshot of a larger population. For example, if a company wants to understand customer satisfaction, it may not be feasible to survey every single customer. Instead, by employing average sampling, the company can randomly select a smaller group of customers to gather their opinions. If this sample is well-chosen, the results can be generalized to reflect the sentiments of the entire customer base.Moreover, average sampling helps in reducing biases that might occur if only certain segments of the population are analyzed. By ensuring that the sample includes individuals from various backgrounds, demographics, and experiences, researchers can achieve more accurate and reliable results. This is particularly important in fields such as healthcare, where understanding diverse patient experiences can lead to better treatment plans and health policies.However, it is essential to note that the effectiveness of average sampling depends on how well the sample represents the population. If the sample is too small or not randomly selected, the findings may not accurately reflect the larger group. This can lead to erroneous conclusions and potentially harmful decisions. Therefore, researchers must carefully design their sampling methods to ensure validity and reliability.Another critical aspect of average sampling is the calculation of averages themselves. When analyzing data, researchers often look at means, medians, and modes to summarize the information collected from their samples. The mean gives an overall average, while the median provides the middle value when the data is ordered, and the mode indicates the most frequently occurring value. Understanding these different types of averages helps researchers interpret their data more effectively.In conclusion, average sampling is a fundamental technique in statistics that enables researchers and decision-makers to gain insights into larger populations without exhaustive analysis. By selecting a representative sample and calculating various averages, they can make informed choices that benefit their organizations and communities. As we continue to navigate an increasingly data-driven world, mastering the principles of average sampling will be essential for anyone involved in research, business, or policy-making. The ability to draw meaningful conclusions from limited data is a powerful skill that can lead to significant advancements in various fields.

在统计分析中，平均抽样的概念在理解数据集和做出明智决策方面起着至关重要的作用。平均抽样是指从更大的人群中选择一部分个体或项目，以估计整个群体的特征或行为的过程。这种方法对于研究人员、企业和政策制定者至关重要，因为它使他们能够得出结论，而不必分析每一个成员，这可能耗时且成本高昂。平均抽样的重要性在于它提供了对更大人群的代表性快照。例如，如果一家公司想了解客户满意度，调查每一位客户可能不可行。相反，通过采用平均抽样，公司可以随机选择一小组客户来收集他们的意见。如果这个样本选择得当，结果就可以推广到整个客户群。此外，平均抽样有助于减少可能发生的偏见，如果只分析某些人群。通过确保样本包括来自不同背景、人口统计和经验的个体，研究人员可以获得更准确和可靠的结果。这在医疗保健等领域尤为重要，因为了解多样化患者的经历可以导致更好的治疗计划和健康政策。然而，必须注意的是，平均抽样的有效性取决于样本如何代表总体。如果样本太小或没有随机选择，结果可能无法准确反映更大的群体。这可能导致错误的结论和潜在的有害决策。因此，研究人员必须仔细设计他们的抽样方法，以确保有效性和可靠性。平均抽样的另一个关键方面是平均值的计算。当分析数据时，研究人员通常会查看均值、中位数和众数，以总结从样本中收集的信息。均值提供整体平均值，而中位数在数据排序时提供中间值，众数则表示出现频率最高的值。理解这些不同类型的平均值有助于研究人员更有效地解释他们的数据。总之，平均抽样是统计学中的一种基本技术，使研究人员和决策者能够在不进行全面分析的情况下获得对更大人群的洞察。通过选择具有代表性的样本并计算各种平均值，他们可以做出有利于其组织和社区的明智选择。随着我们继续在一个日益数据驱动的世界中航行，掌握平均抽样的原则将对任何参与研究、商业或政策制定的人来说都是必不可少的。从有限数据中得出有意义的结论的能力是一项强大的技能，可以在各个领域带来重大进展。