probable error

简明释义

概率误差

英英释义

例句

1.The probable error can help researchers understand the reliability of their data.

在研究中，可能误差可以帮助研究人员理解数据的可靠性。

2.The probable error of the survey results was estimated to be around 3%.

调查结果的可能误差估计约为3%。

3.In statistical analysis, understanding the probable error is crucial for interpreting results.

在统计分析中，理解可能误差对解释结果至关重要。

4.The scientist calculated the probable error to determine the accuracy of his measurements.

科学家计算了可能误差以确定他测量的准确性。

5.She reported the probable error in her findings to ensure transparency.

她在研究结果中报告了可能误差以确保透明度。

作文

In the field of statistics and data analysis, understanding the concept of probable error is crucial. The probable error refers to a measure of the uncertainty or variability associated with a statistical estimate. It is often used to indicate the range within which we can expect the true value of a parameter to lie, given the data we have collected. This concept is essential for researchers and analysts who rely on data to make informed decisions. For instance, consider a scenario where a researcher is conducting a study to determine the average height of adult males in a specific city. After collecting data from a sample of 100 individuals, the researcher calculates the average height to be 175 cm with a probable error of 2 cm. This means that the researcher can be reasonably confident that the true average height of all adult males in that city falls between 173 cm and 177 cm. The significance of probable error extends beyond mere numbers; it embodies the inherent uncertainties in any data-driven analysis. In real-world applications, such as quality control in manufacturing or medical research, understanding the probable error helps stakeholders assess risks and make better decisions. For example, if a pharmaceutical company is testing the efficacy of a new drug, the probable error associated with their findings can influence whether they proceed with further trials or seek regulatory approval. Moreover, the probable error is closely related to confidence intervals, which provide a range of values that likely contain the true parameter. A narrow confidence interval indicates a smaller probable error, suggesting that the estimate is more precise. Conversely, a wider interval indicates greater uncertainty. Understanding these concepts allows statisticians to communicate results effectively and helps non-experts grasp the reliability of the findings. Additionally, the calculation of probable error can be influenced by various factors, including sample size, variability within the data, and the method of data collection. Larger sample sizes typically lead to smaller probable errors, as they provide a more accurate representation of the population. On the other hand, high variability within the data can increase the probable error, reflecting greater uncertainty in the estimates. In conclusion, the concept of probable error is vital in statistics and data analysis, serving as a key indicator of uncertainty. By understanding and applying this concept, researchers and analysts can better interpret their results and convey the reliability of their findings. As data continues to play an increasingly important role in decision-making across various fields, a solid grasp of probable error will undoubtedly enhance our ability to draw meaningful conclusions from the information at hand.

在统计学和数据分析领域，理解“probable error”的概念至关重要。“probable error”指的是与统计估计相关的不确定性或变异性的度量。它通常用于指示我们可以期待某个参数的真实值位于何处的范围，基于我们收集的数据。这个概念对于依赖数据做出明智决策的研究人员和分析师至关重要。例如，考虑一个场景，一个研究人员正在进行一项研究，以确定特定城市成年男性的平均身高。在从100个个体中收集数据后，研究人员计算出平均身高为175厘米，且“probable error”为2厘米。这意味着研究人员可以合理地相信该城市所有成年男性的真实平均身高介于173厘米和177厘米之间。“probable error”的重要性超越了数字本身；它体现了任何基于数据的分析中固有的不确定性。在实际应用中，例如制造业的质量控制或医学研究，理解“probable error”帮助利益相关者评估风险并做出更好的决策。例如，如果一家制药公司正在测试一种新药的有效性，与其发现相关的“probable error”可能会影响他们是否继续进行进一步试验或寻求监管批准。此外，“probable error”与置信区间密切相关，置信区间提供了一个可能包含真实参数的值范围。狭窄的置信区间表明较小的“probable error”，这表明估计更精确。相反，较宽的区间则表示更大的不确定性。理解这些概念使统计学家能够有效地传达结果，并帮助非专家掌握研究结果的可靠性。此外，“probable error”的计算可能受到多种因素的影响，包括样本大小、数据中的变异性以及数据收集方法。较大的样本通常导致较小的“probable error”，因为它们提供了对总体的更准确表示。另一方面，数据中的高变异性可能会增加“probable error”，反映出对估计的更大不确定性。总之，“probable error”的概念在统计学和数据分析中至关重要，作为不确定性的关键指标。通过理解和应用这一概念，研究人员和分析师能够更好地解释他们的结果，并传达他们发现的可靠性。随着数据在各个领域的决策中扮演着越来越重要的角色，对“probable error”的扎实掌握无疑将增强我们从手头信息中得出有意义结论的能力。