coincidence correction

简明释义

符合校正

英英释义

例句

1.In astronomy, coincidence correction is applied to account for random alignments of celestial bodies.

在天文学中，巧合校正用于考虑天体的随机排列。

2.The coincidence correction method is crucial in genomic studies to avoid misleading results.

在基因组研究中，巧合校正方法对避免误导性结果至关重要。

3.Researchers often use coincidence correction when analyzing large datasets to ensure accuracy.

研究人员在分析大数据集时常常使用巧合校正以确保准确性。

4.Using coincidence correction can significantly improve the reliability of scientific experiments.

使用巧合校正可以显著提高科学实验的可靠性。

5.In statistical analysis, applying coincidence correction helps to reduce false positives.

在统计分析中，应用巧合校正有助于减少假阳性。

作文

In the realm of scientific research, especially in fields such as statistics and data analysis, the term coincidence correction refers to the methods used to adjust for the likelihood that observed patterns or relationships may have occurred by chance rather than due to a genuine effect. This concept is crucial because it helps researchers avoid drawing erroneous conclusions from their data. Without proper coincidence correction (偶然性修正), one might mistakenly identify a significant correlation between two variables when, in reality, it could be a mere coincidence. For instance, consider a study that finds a correlation between ice cream sales and the number of drownings. At first glance, one might conclude that buying ice cream causes people to drown, which is absurd. However, both of these variables are likely influenced by a third factor: warm weather. This example illustrates why it is vital to apply coincidence correction in analyses to ensure that we are not misinterpreting data. Researchers often employ various statistical techniques to perform coincidence correction (偶然性修正). One common method is the Bonferroni correction, which adjusts the significance level based on the number of comparisons being made. When multiple hypotheses are tested simultaneously, the chance of finding at least one false positive increases. The Bonferroni correction addresses this by lowering the threshold for significance to account for the number of tests conducted. Another approach is the use of false discovery rate (FDR) procedures, which aim to control the expected proportion of false discoveries among the rejected hypotheses. This method is particularly useful in fields like genomics, where thousands of hypotheses are tested concurrently. By implementing coincidence correction (偶然性修正), researchers can provide more reliable results that reflect true effects rather than random noise. Moreover, the importance of coincidence correction extends beyond just statistical analysis. In the realm of scientific communication, it is essential for researchers to report their findings with transparency and rigor. When publishing results, scientists should clearly state whether they applied any form of coincidence correction (偶然性修正) and how it may have influenced their conclusions. This practice fosters trust in the scientific community and among the public, as it demonstrates a commitment to accuracy and integrity in research. In conclusion, coincidence correction (偶然性修正) is a fundamental aspect of data analysis that ensures researchers draw valid conclusions from their studies. By applying appropriate statistical methods to account for the possibility of chance occurrences, scientists can enhance the reliability of their findings and contribute to the body of knowledge in their respective fields. As we continue to navigate an increasingly data-driven world, understanding and implementing coincidence correction will be essential for advancing science and making informed decisions based on empirical evidence.

在科学研究领域，尤其是在统计学和数据分析等领域，术语偶然性修正指的是用于调整观察到的模式或关系可能是由于偶然而非真正效应的概率的方法。这个概念至关重要，因为它帮助研究人员避免从数据中得出错误的结论。如果没有适当的偶然性修正，人们可能会错误地识别出两个变量之间的显著相关性，而实际上这可能仅仅是巧合。例如，考虑一项研究发现冰淇淋销售与溺水人数之间存在相关性。乍一看，人们可能会得出购买冰淇淋导致人们溺水的结论，但这是荒谬的。然而，这两个变量都可能受到一个第三因素的影响：温暖的天气。这个例子说明了为什么在分析中应用偶然性修正是至关重要的，以确保我们没有误解数据。研究人员通常采用各种统计技术来进行偶然性修正。一种常见的方法是Bonferroni修正，它根据所进行比较的数量来调整显著性水平。当同时测试多个假设时，发现至少一个假阳性的机会增加。Bonferroni修正通过降低显著性的阈值来解决这个问题，以考虑所进行的测试数量。另一种方法是使用假发现率（FDR）程序，旨在控制被拒绝的假设中假发现的预期比例。这个方法在基因组学等领域特别有用，因为成千上万的假设被同时测试。通过实施偶然性修正，研究人员可以提供更可靠的结果，这些结果反映真实的效应，而不是随机噪声。此外，偶然性修正的重要性不仅限于统计分析。在科学传播领域，研究人员在发布结果时必须以透明和严谨的态度进行报告。科学家应该清楚地说明他们是否应用了任何形式的偶然性修正，以及这可能如何影响他们的结论。这种做法促进了科学界和公众之间的信任，因为它展示了对研究准确性和诚信的承诺。总之，偶然性修正是数据分析的基本方面，确保研究人员从他们的研究中得出有效的结论。通过应用适当的统计方法来考虑偶然发生的可能性，科学家可以增强他们发现的可靠性，并为各自领域的知识体系做出贡献。随着我们继续在一个日益数据驱动的世界中航行，理解和实施偶然性修正将对推动科学进步和基于实证证据做出明智决策至关重要。