priori estimates

简明释义

先验估计数,先验估计值

英英释义

例句

1.The engineer provided priori estimates on the time required to complete the construction.

工程师提供了完成施工所需时间的先验估算。

2.In our financial report, we included priori estimates of expected revenue for the next quarter.

在我们的财务报告中，我们包含了下一季度预期收入的先验估算。

3.Before starting the project, we made some priori estimates to determine our budget.

在项目开始之前，我们做了一些先验估算以确定我们的预算。

4.The team used priori estimates to assess the potential risks involved in the new venture.

团队使用先验估算来评估新项目中可能涉及的风险。

5.Using priori estimates, the researchers predicted the outcomes of their experiments.

研究人员利用先验估算预测了实验的结果。

作文

In the realm of scientific research and data analysis, the concept of priori estimates plays a crucial role in forming hypotheses and guiding experiments. These estimates are predictions made based on existing knowledge or theoretical frameworks before any empirical data is collected. For instance, when a scientist sets out to study the effects of a new drug, they may rely on priori estimates derived from previous studies or biological theories to anticipate how the drug will interact with the human body. This initial understanding helps in designing the experiment as well as in determining what data to collect.The significance of priori estimates extends beyond just the design of an experiment; they also influence the interpretation of results. When researchers analyze their findings, they often compare the actual outcomes to their priori estimates. This comparison can reveal whether the initial hypotheses were correct or if there are unforeseen variables at play. For example, if a researcher estimated that a drug would reduce symptoms by 50% based on previous data but found only a 20% reduction, this discrepancy could lead to further investigation into the drug's efficacy and its mechanisms of action.Moreover, priori estimates are not limited to the field of medicine. In economics, for instance, analysts make priori estimates about market trends based on historical data and current economic indicators. These estimates help businesses and governments make informed decisions regarding investments and policy-making. If an economist predicts a recession based on priori estimates of unemployment rates and consumer spending, stakeholders can take proactive measures to mitigate potential negative impacts.Another important aspect of priori estimates is their role in statistical modeling. When creating models to predict future events, statisticians often use priori estimates to set initial parameters. These estimates serve as a foundation upon which more complex models are built. As new data becomes available, the models can be adjusted and refined, but the priori estimates remain a critical starting point. This iterative process highlights the importance of having a solid theoretical basis, as it can significantly affect the accuracy and reliability of the model's predictions.However, relying too heavily on priori estimates can also lead to biases. If researchers or analysts hold onto their initial estimates without being open to new evidence, they may overlook important factors or misinterpret data. This phenomenon, known as confirmation bias, can hinder scientific progress and lead to faulty conclusions. Therefore, while priori estimates are valuable tools in research and analysis, they must be used judiciously, with a willingness to adapt as new information emerges.In conclusion, priori estimates are essential components of research across various fields, providing a framework for hypothesis formation, experimental design, and data interpretation. They enable scientists and analysts to make informed predictions and guide their work effectively. However, it is crucial to remain flexible and responsive to new data, ensuring that these estimates enhance rather than constrain our understanding of complex phenomena. The balance between utilizing priori estimates and remaining open to new evidence is key to advancing knowledge and achieving accurate results in any scientific endeavor.

在科学研究和数据分析领域，priori estimates的概念在形成假设和指导实验中扮演着至关重要的角色。这些估计是基于现有知识或理论框架在收集任何实证数据之前做出的预测。例如，当一位科学家开始研究一种新药的效果时，他们可能依赖于从先前研究或生物理论中得出的priori estimates来预测该药物将如何与人体相互作用。这种初步理解有助于实验设计以及确定要收集的数据。priori estimates的重要性不仅限于实验设计；它们还影响结果的解释。当研究人员分析他们的发现时，他们常常将实际结果与他们的priori estimates进行比较。这种比较可以揭示最初假设是否正确，或者是否存在意想不到的变量。例如，如果一位研究者根据先前的数据估计某种药物将减少50%的症状，但发现仅减少了20%，这种差异可能会导致进一步调查该药物的有效性及其作用机制。此外，priori estimates并不限于医学领域。在经济学中，分析师根据历史数据和当前经济指标对市场趋势进行priori estimates。这些估计帮助企业和政府就投资和政策制定做出明智的决策。如果一位经济学家根据失业率和消费者支出等priori estimates预测经济衰退，利益相关者可以采取主动措施来减轻潜在的负面影响。priori estimates的另一个重要方面是它们在统计建模中的作用。在创建预测未来事件的模型时，统计学家通常使用priori estimates来设置初始参数。这些估计作为构建更复杂模型的基础。随着新数据的出现，模型可以进行调整和完善，但priori estimates仍然是一个关键的起点。这一迭代过程突显了拥有坚实理论基础的重要性，因为它可以显著影响模型预测的准确性和可靠性。然而，过于依赖priori estimates也可能导致偏见。如果研究人员或分析师坚持他们的初始估计而不愿接受新证据，他们可能会忽视重要因素或误解数据。这种现象被称为确认偏见，可能会阻碍科学进步并导致错误结论。因此，尽管priori estimates在研究和分析中是有价值的工具，但必须谨慎使用，并保持适应新信息的意愿。总之，priori estimates是各个领域研究的重要组成部分，为假设形成、实验设计和数据解释提供了框架。它们使科学家和分析师能够做出明智的预测，有效地指导他们的工作。然而，保持灵活性和对新数据的响应至关重要，确保这些估计增强而不是限制我们对复杂现象的理解。在任何科学工作中，在利用priori estimates和保持对新证据开放之间取得平衡是推动知识进步和实现准确结果的关键。