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相关概念视频

Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Sample Proportion and Population Proportion01:20

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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相关实验视频

Updated: Jul 8, 2025

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
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使用大型,非代表性的非概率样本进行描述性推断:生态学家的介绍.

Robin J Boyd1, Gavin B Stewart2, Oliver L Pescott1

  • 1UK Centre for Ecology & Hydrology, Wallingford, UK.

Ecology
|December 13, 2023
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概括
此摘要是机器生成的。

使用辅助变量调整非代表性的生物多样性样本可以提高准确性. 虽然大多数方法在估计工厂占用率趋势时减少了偏差,但完全公正的推断需要知道所有相关变量.

关键词:
偏见 偏见 偏见 偏见 偏见生物多样性监测 生物多样性监测非概率样本是非概率样本.权衡权衡权衡权衡权衡权衡权衡

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科学领域:

  • 生态生态学 生态生态学
  • 环境科学 环境科学
  • 统计建模 统计建模

背景情况:

  • 生物多样性监测依赖于样本数据,这些数据可能不代表性,导致有偏见的推断.
  • 非代表性样本发生在采样位置与非采样位置在关键变量上不同时.
  • 辅助变量,样本包含的常见原因和感兴趣的变量,可以帮助调整样本.

研究的目的:

  • 评估六种调查样本调整方法对非代表性的生物多样性数据的有效性.
  • 用公民科学数据估计英国Calluna vulgaris的平均占用率和趋势.
  • 与未经调整的估计相比,评估调整后估计的准确性.

主要方法:

  • 应用了六种调整技术:亚抽样,准随机化,后分层化,超群建模,双重稳定程序,多层回归和后分层化.
  • 利用一个庞大的,不代表性的公民科学数据集来研究英国Calluna vulgaris的占用率.
  • 1987-1999年和2010-2019年的估计平均占用率,以及这些时期之间的趋势.

主要成果:

  • 大多数调整方法的结果是,与未经调整的数据相比,对平均占用率和趋势的估计更准确.
  • 调整后估计的标准不确定性间隔通常不包括真实值.
  • 调整的有效性取决于仔细选择辅助变量.

结论:

  • 样本调整技术可以显著减少从非代表性数据集的生物多样性监测中的偏见.
  • 如果不了解所有相关的辅助变量,完全不受偏见的推断是无法实现的.
  • 在使用调整样本时,确认和报告潜在的残余偏差至关重要.