two-sample test

简明释义

双样本检验

英英释义

例句

1.In our research, we conducted a two-sample test 双样本检验 to compare the effectiveness of two different medications.

在我们的研究中，我们进行了一个双样本检验 two-sample test 来比较两种不同药物的有效性。

2.We used a two-sample test 双样本检验 to analyze the differences in performance between male and female athletes.

我们使用了一个双样本检验 two-sample test 来分析男性和女性运动员之间的表现差异。

3.The two-sample test 双样本检验 showed that there was a significant difference between the two groups' average scores.

这个双样本检验 two-sample test 显示出两组的平均分数之间存在显著差异。

4.Before launching the new product, we performed a two-sample test 双样本检验 to assess customer satisfaction levels.

在推出新产品之前，我们进行了一个双样本检验 two-sample test 来评估客户满意度。

5.The results of the two-sample test 双样本检验 indicated that the new teaching method was more effective than the traditional one.

这个双样本检验 two-sample test 的结果表明，新教学方法比传统方法更有效。

作文

In the field of statistics, researchers often face the challenge of comparing two different groups to draw meaningful conclusions. One common method used for this purpose is the two-sample test. A two-sample test is a statistical procedure that helps to determine whether there is a significant difference between the means of two independent groups. This technique is vital in various fields such as medicine, psychology, and social sciences, where understanding differences between populations can lead to important insights and decisions.For instance, consider a scenario where a pharmaceutical company wants to test the effectiveness of a new drug. They might conduct a study where one group of patients receives the new medication while another group receives a placebo. After a specified period, the researchers would then compare the health outcomes of both groups using a two-sample test. If the results show a statistically significant difference in recovery rates, the company may conclude that the new drug is effective.The two-sample test can be categorized into two main types: the independent t-test and the Mann-Whitney U test. The independent t-test is used when the data is normally distributed and the variances of the two groups are equal. It calculates the probability that the observed difference in means could have occurred by chance. On the other hand, the Mann-Whitney U test is a non-parametric test that does not assume a normal distribution, making it suitable for ordinal data or non-normally distributed interval data.To perform a two-sample test, researchers must first formulate a null hypothesis, which typically states that there is no difference between the two groups. They also define an alternative hypothesis, which posits that a difference does exist. After collecting the necessary data, they calculate the test statistic and compare it to a critical value from the statistical distribution relevant to their test. If the test statistic exceeds the critical value, the null hypothesis is rejected, indicating that the groups are significantly different.One of the key considerations when conducting a two-sample test is ensuring that the samples are independent. This means that the selection of one sample should not influence the selection of the other. Additionally, researchers must ensure that the sample sizes are adequate to achieve reliable results. Small sample sizes may lead to inconclusive findings, whereas excessively large samples can reveal statistically significant differences that may not be practically meaningful.In conclusion, the two-sample test is an essential tool in statistical analysis, allowing researchers to compare two distinct groups effectively. By understanding how to properly implement and interpret this test, researchers can make informed decisions based on their findings. Whether in clinical trials, social research, or educational assessments, the ability to discern differences between groups is crucial for advancing knowledge and improving practices across various domains. Thus, mastering the two-sample test is an invaluable skill for anyone involved in data analysis and research.

在统计学领域，研究人员常常面临比较两个不同组别以得出有意义结论的挑战。为此，常用的一种方法是两样本检验。两样本检验是一种统计程序，帮助确定两个独立组的均值之间是否存在显著差异。这一技术在医学、心理学和社会科学等多个领域至关重要，因为了解不同人群之间的差异可以带来重要的见解和决策。例如，考虑一个制药公司想要测试一种新药的有效性。他们可能会进行一项研究，其中一组患者接受新药，而另一组患者则接受安慰剂。在指定的时间段后，研究人员将使用两样本检验比较两组的健康结果。如果结果显示恢复率存在统计学上显著的差异，公司可能会得出新药有效的结论。两样本检验可以分为两种主要类型：独立t检验和Mann-Whitney U检验。当数据呈正态分布且两组的方差相等时，使用独立t检验。它计算观察到的均值差异可能偶然发生的概率。另一方面，Mann-Whitney U检验是一种非参数检验，不假设正态分布，适合于顺序数据或不呈正态分布的区间数据。进行两样本检验时，研究人员必须首先制定一个零假设，该假设通常表明两组之间没有差异。他们还定义一个替代假设，假设存在差异。在收集必要的数据后，他们计算检验统计量，并将其与相关检验的临界值进行比较。如果检验统计量超过临界值，则拒绝零假设，表明这两个组之间存在显著差异。进行两样本检验时，确保样本独立性是关键考虑因素。这意味着一个样本的选择不应影响另一个样本的选择。此外，研究人员还必须确保样本大小足够，以获得可靠的结果。小样本可能导致不明确的发现，而过大的样本可能揭示统计上显著的差异，但在实践中可能并不重要。总之，两样本检验是统计分析中的重要工具，使研究人员能够有效比较两个不同的组别。通过理解如何正确实施和解释这一检验，研究人员可以基于他们的发现做出明智的决策。无论是在临床试验、社会研究还是教育评估中，识别组别之间的差异的能力对于推动知识进步和改善各个领域的实践至关重要。因此，掌握两样本检验对任何参与数据分析和研究的人来说都是一项无价的技能。