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相关概念视频

Sample Size Calculation01:19

Sample Size Calculation

3.2K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.2K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

124
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
124
Contaminants and Errors01:16

Contaminants and Errors

85
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
85
Margin of Error01:27

Margin of Error

4.0K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
4.0K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.2K
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...
5.2K
Sampling Plans01:23

Sampling Plans

170
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
170

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Updated: Jun 17, 2025

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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临床研究中的样本大小计算.

Priya Ranganathan1, Vishal Deo2, C S Pramesh3

  • 1Department of Anaesthesiology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India.

Perspectives in clinical research
|August 14, 2024
PubMed
概括
此摘要是机器生成的。

计算正确的样本大小对于可靠和可行的研究至关重要. 本文解释了在各种研究设计中确定样本大小的核心原则.

关键词:
流行病学方法 流行病学方法研究设计研究设计研究样本的大小 样本大小

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科学领域:

  • 生物统计学 生物统计学
  • 研究方法研究方法研究方法学

背景情况:

  • 样本大小的确定对于强大的研究设计至关重要.
  • 样本规模不足可能会损害研究的可靠性和可行性.

研究的目的:

  • 阐明样本大小计算的原则.
  • 引导研究人员为各种研究类型选择合适的样本大小.

主要方法:

  • 对样本大小估计的基本统计原则的审查.
  • 讨论不同研究设计中影响样本大小的因素.

主要成果:

  • 了解关键的统计概念对于准确的样本大小计算至关重要.
  • 在不同的研究方法中,对样本大小的方法有很大差异.

结论:

  • 适当的样本大小计算提高了研究可信度和资源优化.
  • 遵守样本大小原则可以确保研究结果的有效性和通用性.