statistical test sample

简明释义

统计试样

英英释义

例句

1.The statistical test sample was randomly selected to avoid bias in the study.

为了避免研究中的偏见，统计检验样本是随机选择的。

2.The researchers collected a statistical test sample to analyze the effectiveness of the new drug.

研究人员收集了一个统计检验样本来分析新药的有效性。

3.A larger statistical test sample can lead to more reliable results in experiments.

更大的统计检验样本可以在实验中产生更可靠的结果。

4.To validate the findings, we compared our results with another statistical test sample from a different study.

为了验证结果，我们将我们的结果与另一项来自不同研究的统计检验样本进行了比较。

5.Before conducting the survey, we need to determine the size of our statistical test sample.

在进行调查之前，我们需要确定我们的统计检验样本的大小。

作文

In the field of statistics, the term statistical test sample refers to a subset of data drawn from a larger population used to determine the validity of a hypothesis. Understanding this concept is crucial for researchers and analysts who aim to draw meaningful conclusions from their data. A statistical test sample can help in assessing whether the observed effects in the data are significant or if they occurred by chance. To illustrate the importance of a statistical test sample, consider a scenario in which a pharmaceutical company is testing a new drug. Before launching the drug, the company needs to ensure that it is effective and safe for the general population. To do this, they conduct trials using a statistical test sample of participants. This sample should be representative of the larger population to ensure that the results can be generalized. The process begins with defining the population of interest, which includes all individuals who could potentially use the drug. From this population, the researchers select a statistical test sample that includes various demographics such as age, gender, and health status. By ensuring diversity within the sample, the researchers can gather more comprehensive data that reflects the broader population. Once the statistical test sample is collected, researchers apply statistical tests to analyze the data. These tests help determine if there is a statistically significant difference between the group receiving the drug and a control group that receives a placebo. For example, if the results show that the drug group has significantly lower symptoms compared to the placebo group, the researchers may conclude that the drug is effective. However, it is essential to recognize that the validity of the conclusions drawn from the statistical tests heavily depends on the quality of the statistical test sample. If the sample is too small or not representative, the results may lead to incorrect conclusions. This is known as sampling bias, which can occur if certain groups are overrepresented or underrepresented in the statistical test sample. Moreover, researchers must also consider the size of the statistical test sample. A larger sample size generally provides more reliable results because it reduces the margin of error and increases the power of the statistical tests. However, collecting a large sample can be resource-intensive and time-consuming, requiring careful planning and execution. In conclusion, the concept of a statistical test sample is fundamental in the realm of statistics and research. It serves as the backbone for hypothesis testing and helps researchers make informed decisions based on their findings. By carefully selecting and analyzing a statistical test sample, researchers can ensure that their conclusions are valid and applicable to the larger population. As we continue to rely on data-driven decisions in various fields, understanding the significance of a statistical test sample will remain a vital skill for anyone involved in research and analysis.

在统计学领域，术语统计检验样本指的是从较大总体中抽取的数据子集，用于确定假设的有效性。理解这个概念对研究人员和分析师至关重要，他们旨在从数据中得出有意义的结论。统计检验样本可以帮助评估数据中观察到的效果是否显著，或者它们是否偶然出现。为了说明统计检验样本的重要性，考虑一个制药公司测试新药的场景。在推出药物之前，公司需要确保其对一般人群有效且安全。为此，他们使用一组参与者进行试验，这组参与者就是统计检验样本。这个样本应该是代表更大人群的，以确保结果可以推广。这个过程始于定义感兴趣的人群，包括所有可能使用该药物的个体。从这个人群中，研究人员选择一个统计检验样本，其中包括各种人口统计特征，如年龄、性别和健康状况。通过确保样本的多样性，研究人员可以收集更全面的数据，反映更广泛的人群。一旦收集了统计检验样本，研究人员应用统计检验来分析数据。这些检验有助于确定接受药物的组与接受安慰剂的对照组之间是否存在统计上显著的差异。例如，如果结果显示药物组的症状明显低于安慰剂组，研究人员可能会得出该药物有效的结论。然而，必须认识到，从统计检验中得出的结论的有效性在很大程度上依赖于统计检验样本的质量。如果样本太小或不具代表性，结果可能导致错误的结论。这被称为抽样偏差，可能发生在某些群体在统计检验样本中被过度代表或不足代表的情况下。此外，研究人员还必须考虑统计检验样本的大小。较大的样本通常提供更可靠的结果，因为它减少了误差范围并提高了统计检验的效能。然而，收集大量样本可能需要消耗大量资源和时间，因此需要仔细规划和执行。总之，统计检验样本的概念在统计学和研究领域中是基础性的。它作为假设检验的支柱，帮助研究人员根据他们的发现做出明智的决策。通过仔细选择和分析统计检验样本，研究人员可以确保他们的结论是有效的，并适用于更大的人群。随着我们继续在各个领域依赖数据驱动的决策，理解统计检验样本的重要性将仍然是任何参与研究和分析的人的重要技能。