first power estimate

简明释义

第一次功率估算

英英释义

例句

1.The engineer presented the first power estimate 首次功率估算 during the meeting to outline the energy requirements.

工程师在会议上展示了<首次功率估算>以概述能源需求。

2.The first power estimate 首次功率估算 revealed that we need to upgrade our electrical systems.

<首次功率估算>显示我们需要升级电气系统。

3.Our team will provide a first power estimate 首次功率估算 based on the initial design specifications.

我们的团队将根据初步设计规格提供<首次功率估算>。

4.To avoid delays, the contractor requested a first power estimate 首次功率估算 before proceeding with the installation.

为了避免延误，承包商在进行安装之前请求<首次功率估算>。

5.Before starting the project, we need to calculate the first power estimate 首次功率估算 to ensure we have enough resources.

在项目开始之前，我们需要计算<第一功率估算>以确保我们有足够的资源。

作文

In the realm of scientific research and statistical analysis, the concept of a first power estimate plays a crucial role in determining the effectiveness of experiments and studies. The term refers to the initial calculation of the probability that a test will correctly reject a null hypothesis when it is false. Essentially, it helps researchers understand how likely they are to detect an effect if there is one. This estimation is vital for planning studies, as it informs researchers about the sample size needed to achieve reliable results.When designing an experiment, researchers must consider various factors that influence the first power estimate. These include the expected effect size, the significance level (alpha), and the sample size. The effect size is a measure of the strength of the relationship between two variables or the magnitude of the difference between groups. A larger effect size typically increases the power of a study, making it easier to detect significant results.The significance level, often set at 0.05, indicates the threshold for rejecting the null hypothesis. A lower alpha level means that the researcher requires stronger evidence to reject the null hypothesis, which can decrease the power of the test. Therefore, balancing the significance level with the desired power is essential for effective study design.Sample size is another critical component of the first power estimate. Generally, larger sample sizes lead to higher power, as they provide more information and reduce variability. However, increasing the sample size also requires more resources, time, and effort. Thus, researchers must find an optimal sample size that balances practical constraints with the need for sufficient power.To illustrate the importance of the first power estimate, consider a clinical trial testing a new medication. If the researchers underestimate the required sample size or fail to account for the expected effect size, they may end up with a study that lacks sufficient power to detect a meaningful difference between the medication and a placebo. Consequently, even if the medication is effective, the study might yield inconclusive results, leading to wasted resources and potential delays in bringing beneficial treatments to patients.Moreover, the first power estimate is not only relevant in biomedical research but also in various fields such as psychology, education, and social sciences. In these disciplines, the ability to detect effects accurately is paramount for advancing knowledge and informing policy decisions. For example, in educational research, understanding the power of a study can help educators and policymakers determine whether a new teaching method is effective compared to traditional approaches.In conclusion, the first power estimate is a foundational element in the design and interpretation of research studies. By providing insights into the likelihood of detecting true effects, it guides researchers in making informed decisions about sample sizes, significance levels, and overall study design. As the field of research continues to evolve, mastering the concept of the first power estimate will remain essential for conducting robust and impactful studies that contribute to our understanding of the world around us.

在科学研究和统计分析领域，首要效能估计的概念在确定实验和研究的有效性方面发挥着至关重要的作用。该术语指的是对当零假设为假时，检验正确拒绝零假设的概率的初步计算。基本上，它帮助研究人员理解如果存在效果，他们检测到效果的可能性。这一估计对于规划研究至关重要，因为它告知研究人员实现可靠结果所需的样本量。在设计实验时，研究人员必须考虑影响首要效能估计的各种因素。这些因素包括预期效应大小、显著性水平（alpha）和样本量。效应大小是衡量两个变量之间关系强度或组间差异大小的指标。较大的效应大小通常会增加研究的效能，从而更容易检测到显著结果。显著性水平通常设定为0.05，表示拒绝零假设的阈值。较低的alpha水平意味着研究人员需要更强的证据来拒绝零假设，这可能会降低检验的效能。因此，在有效的研究设计中，平衡显著性水平与所需效能是至关重要的。样本量是首要效能估计的另一个关键组成部分。通常，较大的样本量会导致更高的效能，因为它们提供了更多的信息并减少了变异性。然而，增加样本量也需要更多的资源、时间和精力。因此，研究人员必须找到一个最佳样本量，以平衡实际限制与足够效能的需求。为了说明首要效能估计的重要性，考虑一个测试新药物的临床试验。如果研究人员低估了所需的样本量或未能考虑预期的效应大小，他们可能会得到一个缺乏足够效能以检测有意义差异的研究。因此，即使药物有效，该研究也可能产生不确定的结果，导致资源浪费和潜在的延误，使有益的治疗无法及时提供给患者。此外，首要效能估计不仅在生物医学研究中相关，还在心理学、教育和社会科学等各个领域中具有重要意义。在这些学科中，准确检测效应的能力对于推动知识进步和制定政策决策至关重要。例如，在教育研究中，了解研究的效能可以帮助教育工作者和政策制定者确定新的教学方法是否有效于传统方法。总之，首要效能估计是研究设计和解释中的基础元素。通过提供对检测真实效应可能性的见解，它指导研究人员在样本量、显著性水平和整体研究设计方面做出明智的决策。随着研究领域的不断发展，掌握首要效能估计的概念将继续对进行稳健和有影响力的研究至关重要，这些研究将有助于我们理解周围的世界。