selection with equal probability

简明释义

按等概率抽选

英英释义

例句

1.The game show employed selection with equal probability (等概率选择) to select contestants from the audience randomly.

这档游戏节目采用了等概率选择（等概率选择）来随机从观众中挑选参赛者。

2.In a randomized clinical trial, participants were assigned to treatment groups using selection with equal probability (等概率选择) to ensure unbiased results.

在一项随机临床试验中，参与者通过等概率选择（等概率选择）被分配到治疗组，以确保结果不偏倚。

3.In a survey, respondents were chosen using selection with equal probability (等概率选择) to ensure each demographic was represented.

在一项调查中，受访者通过等概率选择（等概率选择）被选中，以确保每个群体都有代表性。

4.The lottery system is designed to give every ticket an equal chance of winning through selection with equal probability (等概率选择).

彩票系统旨在通过等概率选择（等概率选择）给予每张票平等的获胜机会。

5.When choosing a team for the project, the manager used selection with equal probability (等概率选择) to avoid favoritism.

在为项目选择团队时，经理使用了等概率选择（等概率选择）以避免偏袒。

作文

In the world of statistics and probability, the concept of selection with equal probability plays a crucial role in various fields including research, data analysis, and decision-making. This principle refers to the process where each item in a given set has an equal chance of being chosen during a selection process. Understanding this concept is essential for ensuring fairness and objectivity in sampling methods.To illustrate the importance of selection with equal probability, let us consider a simple example involving a lottery system. In a fair lottery, every participant buys a ticket, and each ticket has the same chance of being drawn as the winning ticket. This method of selection ensures that no individual has an unfair advantage over others, thereby promoting a sense of trust and integrity in the process. If a lottery were to utilize biased selection methods, such as favoring tickets sold at specific locations or to certain individuals, it would undermine the entire purpose of the lottery.Another area where selection with equal probability is vital is in scientific research. When researchers conduct experiments, they often need to select a sample from a larger population to draw conclusions. If the sample is not selected with equal probability, the results may be skewed, leading to inaccurate or misleading findings. For instance, if a medical study aims to evaluate the effectiveness of a new drug, it is crucial that participants are randomly selected from the entire population rather than a specific subgroup. This way, the findings can be generalized to the broader population, ensuring that the research is valid and reliable.Moreover, selection with equal probability is also significant in the realm of machine learning and artificial intelligence. Algorithms that rely on random sampling need to ensure that each data point is selected with equal probability to avoid biases that could affect the model's performance. For instance, if a machine learning model is trained on a dataset where certain classes are overrepresented, it may learn to favor those classes, leading to poor performance when applied to real-world situations where the distribution of classes is more balanced.In conclusion, the concept of selection with equal probability is fundamental across various domains. It ensures fairness, reduces bias, and enhances the reliability of outcomes. Whether in lotteries, scientific research, or machine learning, maintaining equal probability in selection processes is essential for achieving accurate and trustworthy results. As we continue to navigate through a world increasingly driven by data and statistical analysis, understanding and implementing selection with equal probability will remain a cornerstone of ethical and effective practices in our decision-making processes.

在统计学和概率论的世界中，等概率选择的概念在研究、数据分析和决策制定等多个领域中发挥着至关重要的作用。这个原则指的是在选择过程中，给定集合中的每个项目都有相同的被选择机会。理解这一概念对于确保抽样方法的公平性和客观性至关重要。为了说明等概率选择的重要性，让我们考虑一个简单的例子，涉及彩票系统。在一个公平的彩票中，每个参与者购买一张票，每张票被抽中的机会与中奖票相同。这种选择方法确保没有个人对其他人拥有不公平的优势，从而促进了过程中的信任和诚信。如果彩票采用偏向性的选择方法，例如偏爱在特定地点或特定个人出售的票，它将破坏彩票的整个目的。另一个等概率选择至关重要的领域是科学研究。当研究人员进行实验时，他们通常需要从更大的人群中选择样本以得出结论。如果样本没有以等概率被选择，结果可能会出现偏差，导致不准确或误导性的发现。例如，如果一项医学研究旨在评估一种新药的有效性，至关重要的是参与者是从整个群体中随机选择，而不是特定的子群体。这样，研究结果可以推广到更广泛的人群，确保研究的有效性和可靠性。此外，等概率选择在机器学习和人工智能领域也很重要。依赖随机抽样的算法需要确保每个数据点以等概率被选择，以避免可能影响模型性能的偏差。例如，如果机器学习模型在一个某些类别过于代表的数据集上进行训练，它可能会倾向于那些类别，导致在应用于现实世界情况时表现不佳，而这些情况的类别分布更加平衡。总之，等概率选择的概念在各个领域都是基础。它确保了公平性，减少了偏见，并增强了结果的可靠性。无论是在彩票、科学研究还是机器学习中，在选择过程中保持等概率都是实现准确和可信结果的关键。随着我们继续在一个越来越受数据和统计分析驱动的世界中航行，理解和实施等概率选择将始终是我们决策过程中伦理和有效实践的基石。