random sampling

简明释义

随机取样

英英释义

例句

1.To analyze the voting patterns, the team employed random sampling 随机抽样 to gather data from various demographics.

为了分析投票模式，团队采用了随机抽样 random sampling 从不同的人口统计中收集数据。

2.The researchers conducted a study using random sampling 随机抽样 to ensure that every individual had an equal chance of being chosen.

研究人员进行了一个研究，使用了随机抽样 random sampling 以确保每个人都有相等的被选中机会。

3.In clinical trials, random sampling 随机抽样 helps in minimizing bias in participant selection.

在临床试验中，随机抽样 random sampling 有助于最小化参与者选择中的偏见。

4.The quality control department uses random sampling 随机抽样 to inspect products off the assembly line.

质量控制部门使用随机抽样 random sampling 来检查生产线上的产品。

5.In a survey of customer satisfaction, we used random sampling 随机抽样 to select participants from our database.

在客户满意度调查中，我们使用了随机抽样 random sampling 从我们的数据库中选择参与者。

作文

In the field of statistics, the concept of random sampling plays a crucial role in ensuring that research findings are valid and reliable. Random sampling refers to a method of selecting a subset of individuals from a larger population in such a way that every individual has an equal chance of being chosen. This technique is essential for minimizing bias in research studies and surveys. By employing random sampling, researchers can obtain a representative sample that accurately reflects the characteristics of the entire population.One of the primary advantages of random sampling is that it enhances the generalizability of research results. When a sample is selected randomly, the findings can be extrapolated to the broader population with greater confidence. For instance, if a researcher conducts a survey on consumer preferences using random sampling, they can confidently assert that their results apply to the entire target market rather than just the individuals who were directly surveyed.Moreover, random sampling helps to reduce sampling error. Sampling error occurs when the sample chosen does not accurately represent the population, leading to skewed results. By ensuring that every member of the population has an equal opportunity to be included in the sample, random sampling minimizes the likelihood of over-representing or under-representing certain groups within the population. This is particularly important in fields such as public health, where accurate data is essential for making informed decisions.There are various methods of random sampling, including simple random sampling, stratified sampling, and cluster sampling. Simple random sampling involves selecting individuals completely at random, often using techniques like lottery systems or random number generators. Stratified sampling, on the other hand, divides the population into distinct subgroups and then randomly samples from each subgroup. This approach ensures that specific segments of the population are adequately represented. Cluster sampling involves dividing the population into clusters, usually based on geographical areas, and then randomly selecting entire clusters for study.Despite its many advantages, random sampling is not without challenges. One significant issue is the accessibility of the population. In some cases, researchers may have difficulty reaching certain groups, which can lead to non-response bias. Additionally, achieving a truly random sample can be logistically challenging, especially in large populations. Researchers must carefully plan their sampling methods to ensure that they remain as random as possible while also being practical.In conclusion, random sampling is a fundamental technique in statistical research that contributes to the validity and reliability of findings. By providing a way to select a representative sample from a larger population, random sampling allows researchers to draw meaningful conclusions and make informed decisions based on their data. While there are challenges associated with implementing random sampling, its benefits far outweigh the drawbacks. As researchers continue to explore complex questions in various fields, the importance of random sampling will only grow, highlighting its essential role in the pursuit of knowledge and understanding.

在统计学领域，随机抽样的概念在确保研究结果的有效性和可靠性方面发挥着至关重要的作用。随机抽样是指从较大人群中选择个体的一个方法，以使每个个体被选择的机会相等。这种技术对于最小化研究研究和调查中的偏差至关重要。通过使用随机抽样，研究人员可以获得一个代表性的样本，该样本准确反映整个群体的特征。随机抽样的主要优点之一是它增强了研究结果的普遍适用性。当样本是随机选择时，研究人员可以更有信心地推断出他们的结果适用于整个目标市场，而不仅仅是直接调查的个体。例如，如果一位研究人员使用随机抽样进行消费者偏好调查，他们可以自信地声称他们的结果适用于整个目标市场。此外，随机抽样有助于减少抽样误差。抽样误差发生在所选样本未能准确代表人群，从而导致结果扭曲。通过确保每个成员都有平等的机会被纳入样本，随机抽样最小化了过度代表或不足代表人群中某些群体的可能性。这在公共卫生等领域尤为重要，因为准确的数据对于做出明智的决策至关重要。随机抽样有多种方法，包括简单随机抽样、分层抽样和集群抽样。简单随机抽样涉及完全随机地选择个体，通常使用抽奖系统或随机数字生成器等技术。另一方面，分层抽样将人群划分为不同的子组，然后从每个子组中随机抽样。这种方法确保了人群中特定部分得到了充分代表。集群抽样涉及将人群划分为集群，通常基于地理区域，然后随机选择整个集群进行研究。尽管有许多优点，随机抽样并非没有挑战。一个重要的问题是人群的可接触性。在某些情况下，研究人员可能难以接触到某些群体，这可能导致非响应偏差。此外，达到真正的随机样本在后勤上可能具有挑战性，尤其是在大型人群中。研究人员必须仔细规划他们的抽样方法，以确保尽可能保持随机性，同时又具备实用性。总之，随机抽样是统计研究中的一项基本技术，有助于研究结果的有效性和可靠性。通过提供一种从较大人群中选择代表性样本的方法，随机抽样使研究人员能够根据数据得出有意义的结论并做出明智的决策。尽管实施随机抽样存在挑战，但其好处远远超过缺点。随着研究人员继续探索各个领域的复杂问题，随机抽样的重要性只会增加，突显其在追求知识和理解中的必要角色。