confidence coefficient

简明释义

置信度

英英释义

例句

1.In machine learning, the model's predictions often include a confidence coefficient 置信系数 to indicate how certain it is about its classifications.

在机器学习中，模型的预测通常包括一个confidence coefficient 置信系数，以表明它对分类结果的确定性。

2.The research study reported a high confidence coefficient 置信系数 of 0.95, indicating strong reliability in the results.

这项研究报告了一个高的confidence coefficient 置信系数 0.95，表明结果的可靠性很强。

3.Statisticians often use a confidence coefficient 置信系数 of 0.90 or higher for their analyses to ensure validity.

统计学家通常在分析中使用0.90或更高的confidence coefficient 置信系数以确保有效性。

4.When conducting surveys, it's important to calculate the confidence coefficient 置信系数 to assess the accuracy of the data collected.

进行调查时，计算confidence coefficient 置信系数 对于评估收集数据的准确性非常重要。

5.A low confidence coefficient 置信系数 can suggest that the sample size was too small to draw reliable conclusions.

低的confidence coefficient 置信系数 可能表明样本量太小，无法得出可靠的结论。

作文

In the realm of statistics and data analysis, the term confidence coefficient plays a crucial role in understanding the reliability of estimates. The confidence coefficient, often expressed as a percentage, indicates the degree of certainty that a parameter lies within a specified range. For instance, a confidence coefficient of 95% suggests that if we were to take multiple samples from a population, approximately 95% of those samples would produce intervals that contain the true population parameter. This concept is fundamental in hypothesis testing, where researchers seek to determine whether their findings are statistically significant or merely due to random chance.The importance of the confidence coefficient extends beyond theoretical applications; it has practical implications in various fields such as medicine, economics, and social sciences. In clinical trials, for example, researchers utilize the confidence coefficient to assess the effectiveness of new drugs. By calculating the confidence coefficient, they can provide stakeholders with a measure of how likely it is that the observed effects are not due to random variation but rather reflect a genuine therapeutic benefit.Moreover, the confidence coefficient aids in decision-making processes. Businesses rely on statistical analyses to make informed choices about product launches, marketing strategies, and resource allocation. A higher confidence coefficient indicates greater certainty in the results, allowing companies to proceed with confidence. Conversely, a low confidence coefficient may prompt further investigation or a reevaluation of strategies.However, it is essential to understand that the confidence coefficient is not an absolute guarantee. A 95% confidence coefficient does not imply that there is a 95% chance that the true parameter lies within the interval; rather, it means that under repeated sampling, 95% of the constructed intervals would capture the true parameter. This distinction is vital for researchers and practitioners to avoid misinterpretations of statistical results.Additionally, the choice of the confidence coefficient level can significantly impact the width of the confidence interval. A higher confidence coefficient, such as 99%, will yield a wider interval, reflecting greater uncertainty about the exact parameter value. In contrast, a lower confidence coefficient, like 90%, results in a narrower interval but at the cost of reduced confidence in capturing the true parameter. Thus, selecting an appropriate confidence coefficient is a balancing act between precision and certainty.In conclusion, the confidence coefficient is a fundamental concept in statistics that quantifies the reliability of estimates and informs decision-making across various fields. Understanding its implications helps researchers and practitioners navigate the complexities of data interpretation and enhances the credibility of their findings. As we continue to rely on data-driven insights in our increasingly complex world, mastering concepts like the confidence coefficient becomes essential for effective analysis and informed decision-making.

在统计学和数据分析领域，术语置信系数在理解估计值的可靠性方面发挥着至关重要的作用。置信系数通常以百分比形式表示，指示参数位于指定范围内的确定程度。例如，95%的置信系数表明，如果我们从一个总体中抽取多个样本，大约95%的样本将产生包含真实总体参数的区间。这个概念在假设检验中是基础，研究人员试图确定他们的发现是否具有统计显著性，或者仅仅是由于随机机会。置信系数的重要性超越了理论应用；它在医学、经济学和社会科学等各个领域都有实际意义。例如，在临床试验中，研究人员利用置信系数来评估新药的有效性。通过计算置信系数，他们可以向利益相关者提供一种衡量观察到的效果不是由于随机变异而反映出真正治疗益处的可能性。此外，置信系数有助于决策过程。企业依赖统计分析来对产品发布、营销策略和资源分配做出明智的选择。较高的置信系数表明结果的确定性更大，使公司能够充满信心地推进。相反，较低的置信系数可能促使进一步调查或重新评估策略。然而，理解置信系数并不是绝对保证。95%的置信系数并不意味着真实参数在区间内的概率为95%；而是说，在重复抽样的情况下，95%的构建区间将捕获真实参数。这个区别对于研究人员和从业者来说至关重要，以避免对统计结果的误解。此外，置信系数水平的选择会显著影响置信区间的宽度。更高的置信系数（如99%）将产生更宽的区间，反映出对确切参数值的更大不确定性。相比之下，较低的置信系数（如90%）导致较窄的区间，但以牺牲捕获真实参数的信心为代价。因此，选择适当的置信系数是一种在精确度和确定性之间的权衡。总之，置信系数是统计学中的基本概念，量化了估计值的可靠性，并为各个领域的决策提供了依据。理解其含义有助于研究人员和从业者应对数据解释的复杂性，并增强其发现的可信度。随着我们在日益复杂的世界中继续依赖数据驱动的洞察，掌握像置信系数这样的概念对于有效分析和明智决策变得至关重要。