sampled data

简明释义

样本数据

英英释义

例句

1.The accuracy of our predictions relies heavily on the quality of the sampled data used.

我们预测的准确性在很大程度上依赖于所使用的抽样数据的质量。

2.During the survey, we gathered sampled data from 1,000 participants.

在调查过程中，我们从1000名参与者那里收集了抽样数据。

3.The sampled data indicated a significant increase in sales over the last quarter.

该抽样数据显示，销售额在上一季度显著增长。

4.In this study, we collected sampled data from various regions to ensure diversity.

在这项研究中，我们从不同地区收集了抽样数据以确保多样性。

5.The researchers analyzed the sampled data to identify trends in consumer behavior.

研究人员分析了抽样数据以识别消费者行为的趋势。

作文

In the age of information technology, the term sampled data refers to a subset of data collected from a larger dataset for analysis. This concept is crucial in various fields such as statistics, machine learning, and data science. Understanding sampled data is essential for anyone who wishes to derive meaningful insights from vast amounts of information. To begin with, sampled data allows researchers and analysts to make inferences about a population without needing to collect data from every single member. For instance, if a company wants to understand customer satisfaction, it may not be feasible to survey every customer. Instead, they can select a representative sample of customers, gather their feedback, and use this sampled data to estimate the satisfaction level of the entire customer base. This method not only saves time and resources but also provides reliable results if the sample is chosen correctly.Moreover, the process of sampling can significantly influence the quality of the sampled data. There are various sampling techniques, such as random sampling, stratified sampling, and cluster sampling. Each method has its advantages and disadvantages, and the choice of technique can affect the validity of the conclusions drawn from the sampled data. For example, random sampling is often preferred because it minimizes bias, ensuring that every individual has an equal chance of being selected. On the other hand, stratified sampling can be beneficial when researchers want to ensure that specific subgroups are adequately represented in the sample.The analysis of sampled data typically involves statistical methods that help identify trends, correlations, and patterns within the data. By applying various statistical tests, analysts can determine whether the findings from the sampled data are statistically significant and can be generalized to the larger population. This step is crucial in making informed decisions based on the data collected.In addition to traditional research, sampled data plays a vital role in the realm of machine learning and artificial intelligence. Algorithms require data to learn and improve their predictions or classifications. Often, these algorithms are trained using sampled data that represents the broader dataset they will eventually encounter. The effectiveness of the model largely depends on the quality and representativeness of the sampled data used during training. If the sample is biased or unrepresentative, the model's performance may suffer, leading to inaccurate predictions.Furthermore, in today’s data-driven world, the ethical considerations surrounding sampled data cannot be overlooked. Researchers must ensure that the data collection process respects privacy and consent, especially when dealing with sensitive information. Transparency in how sampled data is gathered and analyzed is essential to maintain trust with participants and stakeholders alike.In conclusion, sampled data is a powerful tool for extracting insights from large datasets. Its importance spans across various domains, including business, healthcare, and technology. By understanding the principles of sampling and the implications of sampled data, individuals and organizations can make more informed decisions and contribute to advancements in their respective fields. As we continue to navigate an increasingly data-centric world, mastering the concept of sampled data will undoubtedly be a valuable asset for future endeavors.

在信息技术时代，术语sampled data指的是从更大数据集中收集的一个子集，用于分析。这个概念在统计学、机器学习和数据科学等多个领域至关重要。理解sampled data对于任何希望从大量信息中提取有意义见解的人来说都是必不可少的。首先，sampled data允许研究人员和分析师在不需要从每个成员收集数据的情况下，对一个总体进行推断。例如，如果一家公司想要了解客户满意度，调查每位客户可能并不现实。因此，他们可以选择一组具有代表性的客户，收集他们的反馈，并利用这些sampled data来估算整个客户群体的满意度水平。这种方法不仅节省了时间和资源，还能提供可靠的结果，前提是样本的选择是正确的。此外，抽样过程会显著影响sampled data的质量。有多种抽样技术，如随机抽样、分层抽样和聚类抽样。每种方法都有其优缺点，选择的技术会影响从sampled data中得出的结论的有效性。例如，随机抽样通常是首选，因为它最小化了偏差，确保每个个体都有被选择的平等机会。另一方面，当研究人员希望确保特定子群体在样本中得到充分代表时，分层抽样可能会很有利。对sampled data的分析通常涉及统计方法，这些方法帮助识别数据中的趋势、相关性和模式。通过应用各种统计测试，分析师可以确定从sampled data中得出的发现是否具有统计显著性，并且可以推广到更大的总体。这一步骤在根据收集的数据做出明智决策时至关重要。除了传统研究之外，sampled data在机器学习和人工智能领域也发挥着重要作用。算法需要数据来学习和改善其预测或分类。通常，这些算法使用表示它们最终将遇到的更大数据集的sampled data进行训练。模型的有效性在很大程度上依赖于训练过程中使用的sampled data的质量和代表性。如果样本存在偏差或不具代表性，模型的性能可能会受到影响，从而导致不准确的预测。此外，在当今数据驱动的世界中，围绕sampled data的伦理考虑也不容忽视。研究人员必须确保数据收集过程尊重隐私和同意，特别是在处理敏感信息时。如何收集和分析sampled data的透明度对于维护与参与者和利益相关者之间的信任至关重要。总之，sampled data是从大数据集中提取见解的强大工具。它的重要性跨越多个领域，包括商业、医疗保健和技术。通过理解抽样原则及其对sampled data的影响，个人和组织可以做出更明智的决策，并为各自领域的进步做出贡献。随着我们继续在一个越来越以数据为中心的世界中航行，掌握sampled data的概念无疑将成为未来努力的一项宝贵资产。