sampling error

简明释义

取样误差

英英释义

例句

1.To minimize sampling error 抽样误差, we increased our sample size significantly.

为了最小化抽样误差，我们显著增加了样本量。

2.The survey results showed a significant sampling error 抽样误差 due to the small size of the participant group.

调查结果显示，由于参与者组的规模较小，出现了显著的抽样误差。

3.The sampling error 抽样误差 can be reduced by using random sampling techniques.

通过使用随机抽样技术，可以减少抽样误差。

4.Researchers must account for sampling error 抽样误差 when interpreting data from their studies.

研究人员在解读研究数据时必须考虑到抽样误差。

5.The sampling error 抽样误差 in this poll could lead to misleading conclusions about public opinion.

这项民意调查中的抽样误差可能导致对公众意见的误导性结论。

作文

In the field of statistics, understanding the concept of sampling error is crucial for anyone involved in data collection and analysis. A sampling error occurs when a sample chosen for analysis does not accurately represent the entire population from which it is drawn. This discrepancy can lead to incorrect conclusions and potentially misguided decisions based on faulty data. For example, if a researcher wants to understand the average height of adults in a city but only surveys a group of basketball players, the resulting data will likely be skewed due to the unique characteristics of that specific group. Hence, the sampling error in this case would be significant, as it fails to reflect the true diversity of the adult population.To minimize sampling error, researchers often employ various sampling techniques. Random sampling, for instance, involves selecting individuals from the population in such a way that every member has an equal chance of being chosen. This method helps ensure that the sample is representative of the broader population, thereby reducing the potential for sampling error. However, even with random sampling, there is still a possibility of sampling error occurring, particularly if the sample size is too small. A larger sample size generally leads to more reliable results, as it captures a wider range of characteristics present in the population.Another approach to mitigate sampling error is stratified sampling, where the population is divided into distinct subgroups or strata, and samples are taken from each stratum. This ensures that all segments of the population are represented in the sample, further decreasing the likelihood of sampling error. For example, if a study aims to assess student performance across different grade levels, researchers might stratify the sample by grade to ensure that each level is adequately represented.It is also essential to recognize that sampling error is just one type of error that can occur in statistical analysis. Non-sampling errors, such as measurement errors or biases in survey questions, can also significantly impact the validity of research findings. Therefore, while addressing sampling error is vital, it should be part of a broader strategy to enhance the overall quality of data collection and analysis.In conclusion, the concept of sampling error is fundamental in the realm of statistics and research. By understanding what sampling error is and how it can affect research outcomes, practitioners can take necessary steps to reduce its impact. Employing appropriate sampling methods, ensuring adequate sample sizes, and being aware of other potential errors are all critical components of conducting reliable and valid research. Ultimately, minimizing sampling error contributes to more accurate representations of populations and leads to better-informed decisions based on data-driven insights.

在统计学领域，理解抽样误差的概念对于任何参与数据收集和分析的人来说都是至关重要的。抽样误差发生在所选取的样本未能准确代表其所抽取的整个总体时。这种差异可能导致错误的结论，并可能基于错误的数据做出误导性的决策。例如，如果一位研究者想了解一座城市成年人的平均身高，但只调查了一组篮球运动员，那么结果数据很可能会因该特定群体的独特特征而出现偏差。因此，在这种情况下，抽样误差将是显著的，因为它未能反映成年人人口的真实多样性。为了最小化抽样误差，研究人员通常采用各种抽样技术。例如，随机抽样涉及以每个成员都有平等机会被选择的方式，从总体中选择个体。这种方法有助于确保样本代表更广泛的人群，从而减少抽样误差的潜在可能性。然而，即使是随机抽样，仍然存在抽样误差发生的可能性，特别是当样本量过小时。较大的样本量通常会导致更可靠的结果，因为它捕捉到人口中更广泛的特征。另一种减轻抽样误差的方法是分层抽样，其中将总体划分为不同的子群或层，并从每个层中抽取样本。这确保了样本中所有人口段的代表性，从而进一步降低了抽样误差的可能性。例如，如果一项研究旨在评估不同年级学生的表现，研究人员可能会按年级对样本进行分层，以确保每个年级都得到充分代表。还必须认识到，抽样误差只是统计分析中可能发生的一种错误。非抽样误差，例如测量误差或调查问题中的偏见，也可能对研究结果的有效性产生重大影响。因此，在解决抽样误差时，应该作为提高数据收集和分析整体质量的更广泛策略的一部分。总之，抽样误差的概念在统计学和研究领域中是基础性的。通过理解抽样误差是什么以及它如何影响研究结果，实践者可以采取必要措施来减少其影响。采用适当的抽样方法、确保足够的样本量以及意识到其他潜在错误，都是进行可靠和有效研究的关键组成部分。最终，最小化抽样误差有助于更准确地代表人群，并基于数据驱动的洞察做出更好的决策。