power calculation

简明释义

功率计算

英英释义

例句

1.The power calculation 效能计算 showed that we needed at least 200 participants for meaningful results.

该效能计算 power calculation显示我们至少需要200名参与者才能获得有意义的结果。

2.In their report, they included a detailed power calculation 效能计算 to justify their findings.

在他们的报告中，他们包括了详细的效能计算 power calculation以证明他们的发现。

3.Before starting the clinical trial, we conducted a power calculation 效能计算 to determine the sample size needed.

在开始临床试验之前，我们进行了效能计算 power calculation以确定所需的样本大小。

4.He emphasized the importance of conducting a power calculation 效能计算 before finalizing the study design.

他强调在最终确定研究设计之前进行效能计算 power calculation的重要性。

5.The researchers explained that a proper power calculation 效能计算 is essential to avoid underpowered studies.

研究人员解释说，适当的效能计算 power calculation对于避免研究能力不足至关重要。

作文

In the field of statistics, the concept of power calculation plays a crucial role in designing experiments and studies. It refers to the process of determining the sample size required to detect an effect of a given size with a certain degree of confidence. Essentially, power calculation helps researchers understand how likely they are to find a statistically significant result if there is indeed an effect present. This is particularly important in fields such as medicine, psychology, and social sciences where the implications of research findings can have far-reaching consequences.To comprehend the significance of power calculation, one must first understand the concept of statistical power. Statistical power is the probability that a test will correctly reject a false null hypothesis. In simpler terms, it measures the likelihood of finding an effect when there is one to be found. A higher power means a reduced risk of committing a Type II error, which occurs when a study fails to identify a true effect.When planning a study, researchers typically aim for a power level of 80% or higher. This means that there is an 80% chance of detecting an effect if it exists. To achieve this, researchers must consider several factors: the expected effect size, the significance level (commonly set at 0.05), and the variability within the data. The effect size is a measure of the strength of the relationship between two variables; larger effect sizes require smaller sample sizes to achieve the same power.The process of conducting a power calculation involves using statistical formulas or software that take these factors into account. By inputting the expected effect size, significance level, and desired power, researchers can calculate the minimum sample size needed for their study. For example, if a researcher anticipates a small effect size, they may find that they need a much larger sample to achieve adequate power compared to a study expecting a large effect size.Moreover, power calculation is not only essential before a study begins but also serves as a valuable tool during the analysis phase. If a study yields non-significant results, researchers can conduct post-hoc power analyses to determine whether the study was adequately powered to detect the effect. This can help in understanding whether the lack of findings is due to insufficient sample size or if the effect truly does not exist.In conclusion, power calculation is a fundamental aspect of statistical research that helps ensure studies are designed effectively to detect meaningful effects. By carefully considering the necessary sample size based on expected effect sizes and desired power levels, researchers can enhance the validity of their findings and contribute to the body of knowledge in their respective fields. As research continues to evolve, the importance of power calculation will remain a critical component in the quest for reliable and impactful scientific inquiry.

在统计学领域，功效计算的概念在设计实验和研究中起着至关重要的作用。它指的是确定所需样本大小的过程，以便以一定的置信度检测到给定大小的效应。本质上，功效计算帮助研究人员了解，如果确实存在效应，他们发现统计显著结果的可能性有多大。这在医学、心理学和社会科学等领域尤其重要，因为研究发现的影响可能具有深远的后果。要理解功效计算的重要性，首先必须了解统计功效的概念。统计功效是测试正确拒绝错误的零假设的概率。简单来说，它衡量了当存在效应时发现效应的可能性。较高的功效意味着降低犯下第二类错误的风险，即研究未能识别真实效应。在规划研究时，研究人员通常目标设定为80%或更高的功效水平。这意味着如果存在效应，有80%的机会检测到该效应。为了实现这一目标，研究人员必须考虑几个因素：预期效应大小、显著性水平（通常设定为0.05）和数据的变异性。效应大小是衡量两个变量之间关系强度的指标；较大的效应大小需要较小的样本量以达到相同的功效。进行功效计算的过程涉及使用考虑这些因素的统计公式或软件。通过输入预期的效应大小、显著性水平和所需的功效，研究人员可以计算出其研究所需的最小样本大小。例如，如果研究人员预期效应大小较小，他们可能会发现，为了获得足够的功效，需要比预期较大效应大小的研究更大的样本。此外，功效计算不仅在研究开始之前至关重要，而且在分析阶段也作为一个有价值的工具。如果一项研究产生非显著结果，研究人员可以进行事后功效分析，以确定研究是否有足够的功效来检测该效应。这有助于理解缺乏发现是否由于样本量不足，还是效应确实不存在。总之，功效计算是统计研究的基本方面，帮助确保研究设计有效，以检测有意义的效应。通过仔细考虑基于预期效应大小和所需功效水平的必要样本大小，研究人员可以增强其发现的有效性，并为其各自领域的知识体系做出贡献。随着研究的不断发展，功效计算的重要性将继续成为追求可靠和有影响力的科学探究的关键组成部分。