level of significance; significance level

简明释义

有效水平,有效位水平 ［统计］

英英释义

例句

1.In hypothesis testing, the level of significance 显著性水平 helps determine whether to reject the null hypothesis.

在假设检验中，显著性水平 level of significance 有助于决定是否拒绝原假设。

2.A lower level of significance 显著性水平 indicates a stricter criterion for rejecting the null hypothesis.

较低的 显著性水平 level of significance 表示拒绝原假设的标准更严格。

3.When conducting an experiment, it's important to define the significance level 显著性水平 beforehand.

在进行实验时，提前定义 显著性水平 significance level 是很重要的。

4.Researchers often set a significance level 显著性水平 of 0.05 for their studies.

研究人员通常为他们的研究设定 显著性水平 significance level 为0.05。

5.If the p-value is less than the level of significance 显著性水平, we conclude that the results are statistically significant.

如果p值小于 显著性水平 level of significance，我们就得出结果在统计上是显著的结论。

作文

In the realm of statistics, the concept of level of significance is pivotal in hypothesis testing. It serves as a threshold that determines whether to reject the null hypothesis. The significance level, often denoted by the Greek letter alpha (α), is typically set at values such as 0.05, 0.01, or 0.10. This means that if the probability of observing the data, given that the null hypothesis is true, is less than the chosen alpha level, we reject the null hypothesis in favor of the alternative hypothesis. For instance, consider a clinical trial aimed at testing the effectiveness of a new drug. Researchers may set the level of significance at 0.05. If the p-value obtained from the trial data is 0.03, which is less than 0.05, the researchers would conclude that there is sufficient evidence to reject the null hypothesis, suggesting that the drug has a significant effect. Conversely, if the p-value were 0.07, they would fail to reject the null hypothesis, indicating that the evidence is not strong enough to support the drug's effectiveness.The choice of significance level is crucial because it balances the risk of Type I and Type II errors. A Type I error occurs when the null hypothesis is incorrectly rejected, while a Type II error happens when the null hypothesis fails to be rejected when it is false. By setting a lower level of significance, researchers reduce the likelihood of making a Type I error but increase the chance of a Type II error. Therefore, selecting an appropriate significance level requires careful consideration of the context and consequences of potential errors.Moreover, the level of significance does not provide a measure of the strength or importance of an effect; it merely indicates whether the observed effect is statistically significant. This means that a statistically significant result does not necessarily imply a practically significant finding. For example, a study might show a statistically significant difference in blood pressure levels between two groups, but the actual difference might be so small that it has little clinical relevance. Hence, researchers must interpret the results within the broader context of their research question and real-world implications.In conclusion, understanding the level of significance is essential for interpreting statistical results accurately. It plays a critical role in hypothesis testing, guiding researchers in decision-making processes regarding their hypotheses. By carefully selecting and reporting the significance level, researchers can communicate their findings more effectively and contribute to the advancement of knowledge in their respective fields. The level of significance acts as a cornerstone in statistical analysis, highlighting the importance of rigorous methodology and the responsible interpretation of results.

在统计学领域，显著性水平的概念在假设检验中至关重要。它作为一个阈值，决定是否拒绝零假设。显著性水平通常用希腊字母α表示，通常设定为0.05、0.01或0.10等值。这意味着，如果在零假设成立的情况下观察到数据的概率小于所选择的α水平，我们就会拒绝零假设，支持备择假设。例如，考虑一个旨在测试新药有效性的临床试验。研究人员可能将显著性水平设置为0.05。如果从试验数据获得的p值为0.03，低于0.05，研究人员将得出结论，认为有足够的证据拒绝零假设，表明该药物具有显著效果。相反，如果p值为0.07，他们将无法拒绝零假设，表明证据不足以支持药物的有效性。选择显著性水平至关重要，因为它平衡了第一类错误和第二类错误的风险。第一类错误发生在错误地拒绝零假设时，而第二类错误发生在未能拒绝实际上是错误的零假设时。通过设定较低的显著性水平，研究人员降低了发生第一类错误的可能性，但增加了发生第二类错误的机会。因此，选择合适的显著性水平需要仔细考虑上下文及潜在错误的后果。此外，显著性水平并不提供效果强度或重要性的度量；它仅指示观察到的效果在统计上是否显著。这意味着，统计显著的结果并不一定意味着实际显著的发现。例如，一项研究可能显示两组之间的血压水平差异在统计上显著，但实际差异可能微乎其微，以至于在临床上几乎没有相关性。因此，研究人员必须在更广泛的研究问题和现实世界影响的背景下解读结果。总之，理解显著性水平对于准确解读统计结果至关重要。它在假设检验中扮演着关键角色，指导研究人员在决策过程中对其假设进行判断。通过仔细选择和报告显著性水平，研究人员可以更有效地传达他们的发现，并为各自领域知识的进步做出贡献。显著性水平作为统计分析的基石，突显了严格的方法论和负责任地解读结果的重要性。