confidence limits

简明释义

置信区间

英英释义

例句

1.In clinical trials, researchers often report the confidence limits 置信界限 for the effectiveness of a new drug.

在临床试验中，研究人员通常报告新药有效性的confidence limits 置信界限。

2.The financial analyst provided a report that included the confidence limits 置信区间 for the projected revenue growth.

金融分析师提供了一份报告，其中包括预计收入增长的confidence limits 置信区间。

3.When conducting a poll, it is important to present the confidence limits 置信区间 to give context to the results.

在进行民意调查时，重要的是要呈现confidence limits 置信区间以为结果提供背景。

4.The survey results indicate that the average satisfaction score is 75%, with a 95% confidence interval of 70% to 80%, which means the confidence limits 置信区间 are quite narrow.

调查结果显示，平均满意度得分为75%，95%的confidence limits 置信区间为70%到80%，这意味着置信区间相当窄。

5.The confidence limits 置信界限 of the estimated mean temperature change were found to be between 1.5 and 3 degrees Celsius.

估计的平均温度变化的confidence limits 置信界限被发现介于1.5到3摄氏度之间。

作文

In the field of statistics, understanding the concept of confidence limits is crucial for interpreting data accurately. Confidence limits refer to the range of values within which we can expect a population parameter to lie with a certain level of confidence, typically expressed as a percentage. For instance, if we say that the average height of adult males in a city is 175 cm with confidence limits of 170 cm to 180 cm at a 95% confidence level, it means we are 95% confident that the true average height falls within this range.The importance of confidence limits cannot be overstated. In scientific research, for example, researchers often rely on sample data to make inferences about a larger population. The confidence limits provide a way to quantify the uncertainty associated with these estimates. By using confidence limits, researchers can communicate how precise their estimates are and how much variability there is in the data.When conducting experiments or surveys, one must consider the sample size, variability in the data, and the desired level of confidence. Larger sample sizes generally lead to narrower confidence limits, indicating greater precision. Conversely, smaller samples may result in wider confidence limits, reflecting increased uncertainty. This relationship highlights the importance of adequate sampling in research design.Moreover, confidence limits play a vital role in decision-making processes across various fields, including medicine, economics, and social sciences. For instance, in clinical trials, the effectiveness of a new drug is often evaluated using confidence limits. If the confidence limits for the drug’s effectiveness do not include zero, researchers can conclude that the drug has a statistically significant effect.In addition to their application in hypothesis testing, confidence limits also assist in creating predictive models. When forecasting future trends based on historical data, analysts use confidence limits to indicate the range of possible outcomes. This is particularly useful in fields like finance and environmental science, where uncertainty is inherent. By presenting forecasts with confidence limits, analysts can better inform stakeholders about potential risks and uncertainties.To illustrate the practical application of confidence limits, let’s consider a case study involving a new educational program aimed at improving student performance. Researchers might collect data from a sample of students before and after implementing the program. After analyzing the results, they find that the average improvement in test scores is 10 points, with confidence limits ranging from 5 to 15 points at a 95% confidence level. This indicates that while the program appears effective, there is still some uncertainty regarding the exact impact.In conclusion, confidence limits are an essential tool in statistical analysis, providing a framework for understanding the uncertainty associated with estimates. They help researchers, analysts, and decision-makers interpret data more effectively, allowing for informed conclusions and actions. Whether in scientific research, business forecasting, or public policy, grasping the concept of confidence limits is vital for anyone working with data. By acknowledging the limitations of our estimates and expressing them through confidence limits, we enhance the credibility and reliability of our findings.

在统计学领域，理解置信区间的概念对于准确解读数据至关重要。置信区间是指我们可以期望一个总体参数在一定置信水平下位于的值范围，通常以百分比表示。例如，如果我们说某城市成年男性的平均身高为175厘米，且在95%的置信水平下，其置信区间为170厘米到180厘米，这意味着我们有95%的信心认为真实的平均身高落在这个范围内。置信区间的重要性不容小觑。在科学研究中，研究人员通常依赖样本数据对更大的人群进行推断。置信区间提供了一种量化与这些估计相关的不确定性的方法。通过使用置信区间，研究人员可以传达他们的估计有多精确，以及数据中存在多少变异性。在进行实验或调查时，必须考虑样本大小、数据变异性和所需的置信水平。较大的样本通常会导致较窄的置信区间，表明更高的精度。相反，较小的样本可能会导致较宽的置信区间，反映出更大的不确定性。这种关系强调了在研究设计中充分取样的重要性。此外，置信区间在医学、经济学和社会科学等多个领域的决策过程中也发挥着至关重要的作用。例如，在临床试验中，通常通过置信区间来评估新药的有效性。如果药物有效性的置信区间不包括零，研究人员可以得出该药物具有统计学显著效果的结论。除了在假设检验中的应用外，置信区间还帮助创建预测模型。在基于历史数据预测未来趋势时，分析师使用置信区间来指示可能结果的范围。这在金融和环境科学等领域尤为重要，因为不确定性是固有的。通过以置信区间呈现预测，分析师可以更好地向利益相关者通报潜在的风险和不确定性。为了说明置信区间的实际应用，我们考虑一个关于新教育项目的案例研究，该项目旨在提高学生的表现。研究人员可能会在实施该项目之前和之后收集一组学生的数据。在分析结果后，他们发现测试分数的平均提高为10分，且在95%的置信水平下，置信区间范围为5到15分。这表明尽管该项目似乎有效，但仍然存在一些关于确切影响的不确定性。总之，置信区间是统计分析中的一个基本工具，为理解与估计相关的不确定性提供了框架。它们帮助研究人员、分析师和决策者更有效地解读数据，从而得出明智的结论和行动。无论是在科学研究、商业预测还是公共政策中，掌握置信区间的概念对于任何处理数据的人来说都是至关重要的。通过承认我们的估计的局限性并通过置信区间表达出来，我们增强了我们发现的可信度和可靠性。