one-sample design

简明释义

单样本设计

英英释义

例句

1.In clinical trials, a one-sample design 单样本设计 can help assess the effectiveness of a new medication on a selected group of patients.

在临床试验中，one-sample design 单样本设计可以帮助评估新药对特定患者群体的有效性。

2.In a psychology study, researchers used a one-sample design 单样本设计 to assess the average stress levels of college students.

在一项心理学研究中，研究人员使用了一个one-sample design 单样本设计来评估大学生的平均压力水平。

3.The marketing team conducted a survey utilizing a one-sample design 单样本设计 to determine customer preferences for a specific service.

市场团队利用one-sample design 单样本设计进行了一项调查，以确定客户对特定服务的偏好。

4.The team implemented a one-sample design 单样本设计 to compare the test scores of a single class against the national average.

团队实施了一个one-sample design 单样本设计，以将一个班级的测试分数与全国平均水平进行比较。

5.Using a one-sample design 单样本设计, the researchers collected data from 100 participants to evaluate their satisfaction with a new product.

研究人员使用one-sample design 单样本设计从100名参与者那里收集数据，以评估他们对新产品的满意度。

作文

In the field of statistics, research design plays a crucial role in determining the validity and reliability of study results. One common type of research design is the one-sample design, which is particularly useful when researchers want to make inferences about a population based on a single sample. The one-sample design involves collecting data from one group and comparing it to a known standard or population parameter. This method is often employed in various fields, including psychology, medicine, and social sciences, where understanding a specific characteristic of a population is essential.To illustrate the concept of one-sample design, consider a scenario where a psychologist wants to determine if the average test score of their students is significantly different from the national average. In this case, the national average serves as the population parameter, while the students' test scores represent the single sample. By applying statistical tests, such as the t-test, the psychologist can analyze whether the sample mean differs from the national average. If the results are statistically significant, the psychologist can conclude that their students perform differently than expected.The strength of the one-sample design lies in its simplicity and efficiency. Researchers can quickly gather data and analyze it without the need for complex comparisons between multiple groups. However, it is essential to recognize the limitations of this design. For instance, the findings may not be generalizable to other populations, as the sample may not adequately represent the larger group. Moreover, biases in sample selection can lead to skewed results, highlighting the importance of careful sampling methods.Another critical aspect of the one-sample design is the assumption of normality. Statistical tests commonly used with this design assume that the data follows a normal distribution. Therefore, researchers must ensure that their sample size is adequate to meet this assumption or utilize non-parametric tests when necessary. This consideration is vital to ensure the robustness of the conclusions drawn from the analysis.In practical applications, the one-sample design can be seen in various studies. For example, in clinical trials, researchers might want to assess the effectiveness of a new drug by comparing the recovery rates of patients who received the treatment to a known recovery rate from previous studies. Here, the known recovery rate acts as the population parameter, and the patients receiving the new treatment form the sample. Through rigorous testing and analysis, researchers can make informed decisions about the drug's efficacy.In conclusion, the one-sample design is a fundamental research methodology that allows researchers to draw conclusions about a population based on a single sample. Its straightforward approach makes it an attractive option for many studies, but researchers must remain vigilant about its limitations and assumptions. By understanding the intricacies of the one-sample design, researchers can effectively apply this method to yield meaningful insights and contribute to their respective fields of study.

在统计学领域，研究设计在确定研究结果的有效性和可靠性方面起着至关重要的作用。一个常见的研究设计类型是单样本设计，当研究人员希望根据单个样本对总体进行推断时，这种设计特别有用。单样本设计涉及从一个组收集数据，并将其与已知标准或总体参数进行比较。这种方法通常应用于心理学、医学和社会科学等多个领域，在这些领域中，了解特定人群的特征至关重要。为了说明单样本设计的概念，考虑一个心理学家的场景，他们想要确定自己学生的平均考试成绩是否显著不同于全国平均水平。在这种情况下，全国平均水平充当总体参数，而学生的考试成绩则代表单个样本。通过应用统计检验，例如t检验，心理学家可以分析样本均值是否与全国平均水平不同。如果结果具有统计显著性，心理学家可以得出结论，认为他们的学生表现与预期不同。单样本设计的优势在于其简单性和高效性。研究人员可以快速收集数据并进行分析，而无需对多个组进行复杂比较。然而，必须认识到这种设计的局限性。例如，研究结果可能无法推广到其他人群，因为样本可能无法充分代表更大的群体。此外，样本选择中的偏差可能导致结果扭曲，这突显了谨慎采样方法的重要性。单样本设计的另一个关键方面是正态性假设。与此设计常用的统计检验假设数据遵循正态分布。因此，研究人员必须确保样本量足够，以满足这一假设，或在必要时使用非参数检验。这个考虑对于确保从分析中得出的结论的稳健性至关重要。在实际应用中，单样本设计可以在各种研究中看到。例如，在临床试验中，研究人员可能希望通过将接受治疗的患者的康复率与先前研究的已知康复率进行比较来评估新药的有效性。在这里，已知的康复率作为总体参数，而接受新治疗的患者形成样本。通过严格的测试和分析，研究人员可以对药物的有效性做出明智的决定。总之，单样本设计是一种基本的研究方法论，使研究人员能够基于单个样本对总体得出结论。其直接的方法使其成为许多研究的有吸引力的选择，但研究人员必须始终关注其局限性和假设。通过理解单样本设计的复杂性，研究人员可以有效地应用这种方法，产生有意义的见解，并为各自的研究领域做出贡献。