level of significance

简明释义

显著性水平

英英释义

例句

1.A lower level of significance 显著性水平 such as 0.01 indicates a stricter criterion for rejecting the null hypothesis.

较低的显著性水平 level of significance（如0.01）表示拒绝零假设的标准更严格。

2.In clinical trials, a level of significance 显著性水平 of 0.05 is commonly used to assess treatment effectiveness.

在临床试验中，通常使用显著性水平 level of significance 为0.05来评估治疗效果。

3.When conducting an experiment, it's crucial to decide on the level of significance 显著性水平 before analyzing the data.

在进行实验时，决定分析数据之前的显著性水平 level of significance 是至关重要的。

4.In hypothesis testing, researchers often set a level of significance 显著性水平 at 0.05 to determine if results are statistically significant.

在假设检验中，研究人员通常将显著性水平 level of significance 设置为0.05，以确定结果是否具有统计显著性。

5.The level of significance 显著性水平 helps researchers avoid Type I errors when making conclusions from their data.

在从数据中得出结论时，显著性水平 level of significance 有助于研究人员避免第一类错误。

作文

In the realm of statistics, the concept of level of significance is crucial for hypothesis testing. It serves as a threshold that helps researchers determine whether to reject the null hypothesis or not. The level of significance is often denoted by the symbol alpha (α) and is usually set at values such as 0.05, 0.01, or 0.10. This means that there is a 5%, 1%, or 10% risk of concluding that a difference exists when there is no actual difference. Understanding the level of significance is essential for interpreting statistical results accurately. For instance, if a researcher sets the level of significance at 0.05, they are willing to accept a 5% chance of making a Type I error, which occurs when the null hypothesis is incorrectly rejected. This implies that if the p-value obtained from the statistical test is less than 0.05, the researcher can confidently reject the null hypothesis and conclude that there is a statistically significant effect or difference. However, it is important to note that a lower level of significance (e.g., 0.01) indicates a stricter criterion for rejecting the null hypothesis. This means that the researcher requires stronger evidence before making a claim about a significant effect. Conversely, a higher level of significance (e.g., 0.10) allows for more leniency, increasing the likelihood of detecting an effect but also raising the chances of a Type I error. The choice of level of significance often depends on the specific field of study and the consequences of making errors. In medical research, for example, a lower level of significance may be preferred to minimize the risk of claiming that a new treatment is effective when it is not. On the other hand, in exploratory research, a higher level of significance might be acceptable to identify potential areas for further investigation. Furthermore, researchers must communicate their chosen level of significance clearly in their studies. This transparency allows others to understand the context of the findings and assess the reliability of the conclusions drawn. Additionally, it is vital to consider the practical implications of the level of significance. A statistically significant result does not always imply that the effect is practically significant. Therefore, researchers should complement their findings with effect sizes and confidence intervals to provide a more comprehensive understanding of the results. In conclusion, the level of significance is a fundamental concept in statistical analysis that guides researchers in making informed decisions about their hypotheses. By setting an appropriate level of significance, researchers can balance the risks of Type I and Type II errors while ensuring that their findings contribute meaningfully to the body of knowledge in their respective fields. Ultimately, a well-considered level of significance enhances the rigor and credibility of research, leading to more reliable conclusions and better-informed decisions in practice.

在统计学领域，显著性水平的概念对于假设检验至关重要。它作为一个阈值，帮助研究人员决定是否拒绝零假设。显著性水平通常用希腊字母alpha（α）表示，通常设定为0.05、0.01或0.10等值。这意味着有5%、1%或10%的风险得出存在差异的结论，而实际上并不存在差异。理解显著性水平对于准确解读统计结果至关重要。例如，如果研究人员将显著性水平设定为0.05，他们愿意接受5%的概率来犯第一类错误，即错误地拒绝零假设。这意味着如果从统计检验中获得的p值小于0.05，研究人员可以自信地拒绝零假设，并得出存在统计显著效应或差异的结论。然而，需要注意的是，较低的显著性水平（例如0.01）表示拒绝零假设的标准更严格。这意味着研究人员需要更强的证据才能声称存在显著效应。相反，较高的显著性水平（例如0.10）允许更宽松的条件，增加了检测到效应的可能性，但也提高了第一类错误的几率。显著性水平的选择通常取决于特定研究领域和错误后果。在医学研究中，例如，可能更倾向于选择较低的显著性水平以最小化声称新治疗有效时的风险。另一方面，在探索性研究中，较高的显著性水平可能是可以接受的，以识别潜在的进一步研究领域。此外，研究人员必须在研究中清晰沟通他们选择的显著性水平。这种透明度使他人能够理解研究结果的背景，并评估得出的结论的可靠性。此外，考虑显著性水平的实际影响至关重要。统计显著的结果并不总是意味着该效应在实践中显著。因此，研究人员应通过效应大小和置信区间来补充他们的发现，以提供对结果的更全面理解。总之，显著性水平是统计分析中的基本概念，指导研究人员在假设上做出明智的决策。通过设定适当的显著性水平，研究人员可以平衡第一类和第二类错误的风险，同时确保他们的发现对各自领域的知识体系作出有意义的贡献。最终，经过深思熟虑的显著性水平增强了研究的严谨性和可信度，导致更可靠的结论和更明智的实践决策。