biased sample

简明释义

有偏样本

英英释义

例句

1.The marketing team realized their focus group was a biased sample, consisting mostly of loyal customers.

市场团队意识到他们的焦点小组是一个有偏样本，主要由忠实客户组成。

2.To ensure validity, it's essential to avoid a biased sample in your data collection process.

为了确保有效性，在数据收集过程中避免出现有偏样本是至关重要的。

3.The survey results were skewed because they were based on a biased sample of only young adults.

调查结果受到影响，因为它是基于仅包含年轻成年人的有偏样本。

4.The study's findings were questioned due to the presence of a biased sample in the participant selection.

由于参与者选择中存在有偏样本，该研究的结果受到质疑。

5.In scientific research, using a biased sample can lead to inaccurate conclusions.

在科学研究中，使用有偏样本可能导致不准确的结论。

作文

In the field of statistics and research, the concept of a biased sample is crucial to understand. A biased sample refers to a subset of individuals chosen from a larger population in such a way that it does not accurately represent the entire population. This can lead to misleading conclusions and results, as the data collected may reflect the characteristics of the sample rather than those of the whole group. For instance, if a survey is conducted only among college students to determine the average income of adults in a city, the results will likely be skewed. College students typically have lower incomes compared to older adults who are established in their careers. Hence, the sample is biased towards a younger demographic, which does not provide an accurate picture of the overall adult population.Understanding how biased samples occur is essential for researchers and statisticians. Bias can arise from various factors, including the method of selection, the timing of the survey, or even the questions asked. If researchers use convenience sampling, where they select individuals who are easiest to reach, they may inadvertently create a biased sample. For example, conducting a poll in a specific neighborhood may yield results that do not reflect the opinions of the entire city.Furthermore, the implications of using a biased sample can be significant. Decisions based on flawed data can lead to poor policy-making, ineffective marketing strategies, and misallocation of resources. For example, if a company uses a biased sample to gauge customer satisfaction, they may overlook critical issues affecting a large portion of their clientele. Consequently, they may fail to implement necessary changes, leading to decreased customer loyalty and potential loss of revenue.To mitigate the risks associated with biased samples, researchers should strive for random sampling techniques. Random sampling involves selecting individuals from the population in such a way that each member has an equal chance of being chosen. This helps ensure that the sample reflects the diversity of the population, reducing the likelihood of bias. Additionally, researchers should be transparent about their sampling methods and acknowledge any limitations in their studies. By doing so, they can provide context for their findings and allow others to interpret the results more accurately.In conclusion, recognizing and addressing biased samples is a fundamental aspect of conducting reliable research. Researchers must be diligent in their sampling methods to avoid drawing incorrect conclusions that could have far-reaching consequences. As we navigate an increasingly data-driven world, the importance of accurate representation in research cannot be overstated. Only by ensuring that our samples are truly representative can we hope to gain insights that are valid and applicable to the broader population. Therefore, understanding the concept of a biased sample and its implications is essential for anyone involved in research, policy-making, or business decision-making.

在统计学和研究领域，理解“偏倚样本”的概念至关重要。“偏倚样本”是指从一个较大的人群中选择的个体子集，这种选择方式无法准确代表整个群体。这可能导致误导性的结论和结果，因为收集到的数据可能反映样本的特征，而不是整个群体的特征。例如，如果一项调查仅在大学生中进行，以确定某城市成年人的平均收入，那么结果可能会出现偏差。大学生的收入通常低于职业生涯已经建立的成年人。因此，这个样本偏向于年轻人群体，无法提供整体成年人口的准确情况。理解如何产生偏倚样本对于研究人员和统计学家至关重要。偏倚可能源于多种因素，包括选择方法、调查时间甚至所提问的问题。如果研究人员使用方便抽样，即选择最容易接触到的个体，他们可能不经意间创建了一个偏倚样本。例如，在特定社区进行民意调查可能会产生不反映整个城市意见的结果。此外，使用偏倚样本的影响可能是显著的。基于错误数据做出的决策可能导致糟糕的政策制定、无效的营销策略和资源的错误分配。例如，如果一家公司使用偏倚样本来评估客户满意度，他们可能会忽视影响大量客户的关键问题。因此，他们可能无法实施必要的变化，导致客户忠诚度下降和潜在的收入损失。为了降低与偏倚样本相关的风险，研究人员应努力采用随机抽样技术。随机抽样涉及以每个成员都有相等机会被选择的方式，从人群中选择个体。这有助于确保样本反映出人群的多样性，减少偏见的可能性。此外，研究人员应对其抽样方法保持透明，并承认研究中的任何局限性。通过这样做，他们可以为研究结果提供背景，使他人能够更准确地解读结果。总之，识别和解决偏倚样本是进行可靠研究的基本方面。研究人员必须在抽样方法上保持谨慎，以避免得出可能产生深远后果的错误结论。在我们逐渐进入数据驱动的世界时，研究中准确代表的重要性不容小觑。只有确保我们的样本真实代表，我们才能希望获得有效且适用于更广泛人群的见解。因此，理解偏倚样本的概念及其影响对于任何参与研究、政策制定或商业决策的人来说都是至关重要的。