expected error

简明释义

预期误差time of delivery预计交船期

英英释义

例句

1.The developer explained the expected error 预期错误 that users might face after the new feature launch.

开发者解释了用户在新功能发布后可能会遇到的expected error 预期错误。

2.In the simulation, we encountered an expected error 预期错误 that was documented in the user manual.

在模拟中，我们遇到了一个expected error 预期错误，该错误在用户手册中有记录。

3.The team prepared for the expected error 预期错误 by running tests beforehand.

团队通过提前进行测试来准备应对这个expected error 预期错误。

4.The software update will likely cause an expected error 预期错误 during installation.

软件更新在安装过程中可能会导致一个expected error 预期错误。

5.During the data migration, an expected error 预期错误 occurred, but it was resolved quickly.

在数据迁移过程中，发生了一个expected error 预期错误，但很快就解决了。

作文

In the field of data analysis and statistics, the term expected error refers to the anticipated amount of error that may occur in a measurement or prediction. Understanding this concept is crucial for researchers and analysts as it helps them assess the reliability and accuracy of their results. When we conduct experiments or collect data, we often encounter various sources of error, such as measurement errors, sampling errors, and model inaccuracies. The expected error provides a framework for estimating how much these errors might affect our conclusions.For instance, consider a scenario where a scientist is measuring the height of a plant species in a controlled environment. The scientist uses a ruler to take measurements, but there are several factors that could lead to inaccuracies. The ruler could be misaligned, the plant could be slightly bent, or even the environmental conditions could cause fluctuations in growth. By calculating the expected error, the scientist can better understand the range of possible heights and the confidence level of their findings.Moreover, in predictive modeling, the expected error plays a significant role in determining the effectiveness of the model. For example, if a machine learning algorithm is developed to predict housing prices based on various features such as location, size, and amenities, the expected error will indicate how accurate these predictions are likely to be. A high expected error suggests that the model may not be reliable, prompting the developer to refine the algorithm or gather more data to improve its performance.Understanding expected error also aids in decision-making processes. In business, companies often rely on statistical analyses to forecast sales, manage inventory, and evaluate risks. By incorporating the expected error into their analyses, businesses can make more informed decisions and better prepare for uncertainties. For example, if a company estimates that a new product will generate $100,000 in revenue but acknowledges an expected error of ±10%, they can plan their budget and resources accordingly, knowing that actual revenue could vary significantly.Furthermore, the concept of expected error emphasizes the importance of transparency in reporting research findings. Researchers are encouraged to disclose the potential errors associated with their studies, allowing others to critically evaluate the validity of their claims. This practice fosters a culture of accountability and rigor in scientific inquiry, ensuring that findings are not overstated or misinterpreted.In conclusion, the idea of expected error is integral to various fields that rely on data and statistical analysis. It serves as a vital tool for understanding the limitations of measurements and predictions. By acknowledging and calculating the expected error, researchers, analysts, and decision-makers can enhance the reliability of their work, make informed choices, and contribute to a more robust understanding of the phenomena they study. Ultimately, embracing the concept of expected error allows for greater accuracy in interpretation and application of data, leading to more effective outcomes in both research and practical settings.

在数据分析和统计学领域，术语预期误差指的是在测量或预测中可能发生的预期误差量。理解这一概念对研究人员和分析师至关重要，因为它有助于他们评估结果的可靠性和准确性。当我们进行实验或收集数据时，常常会遇到各种误差来源，如测量误差、抽样误差和模型不准确性。预期误差提供了一个框架，用于估计这些误差可能对我们的结论产生多大影响。例如，考虑一个场景，科学家在受控环境中测量一种植物物种的高度。科学家使用尺子进行测量，但可能导致不准确的因素有很多。尺子可能未对齐，植物可能稍微弯曲，甚至环境条件可能导致生长波动。通过计算预期误差，科学家可以更好地理解可能的高度范围及其发现的置信水平。此外，在预测建模中，预期误差在确定模型的有效性方面也起着重要作用。例如，如果开发了一种机器学习算法来预测基于位置、大小和设施等各种特征的房价，预期误差将表明这些预测的准确性。如果预期误差较高，则表明该模型可能不可靠，从而促使开发者优化算法或收集更多数据以提高其性能。理解预期误差还有助于决策过程。在商业中，公司通常依赖统计分析来预测销售、管理库存和评估风险。通过将预期误差纳入分析，公司可以做出更明智的决策，并更好地为不确定性做准备。例如，如果一家公司估计新产品将产生100,000美元的收入，但承认预期误差为±10%，则可以相应地规划预算和资源，知道实际收入可能会有显著变化。此外，预期误差的概念强调了在报告研究结果时透明度的重要性。鼓励研究人员披露与其研究相关的潜在误差，从而使他人能够批判性地评估其主张的有效性。这种做法促进了科学探究中的责任感和严谨性文化，确保研究结果不会被夸大或误解。总之，预期误差的概念对于依赖数据和统计分析的各个领域至关重要。它作为理解测量和预测局限性的关键工具。通过承认和计算预期误差，研究人员、分析师和决策者可以提高其工作的可靠性，做出明智的选择，并为他们研究的现象做出更稳健的理解。最终，接受预期误差的概念可以提高数据解释和应用的准确性，从而在研究和实际环境中实现更有效的结果。