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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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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...
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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.
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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Power01:08

Power

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The concept of work involves force and displacement; meanwhile, the work-energy theorem relates the net work done on a body to the difference in its kinetic energy, calculated between two points on its trajectory. While none of these quantities or relations involves time explicitly, we know that the time available to accomplish work is often just as important as the amount of work itself. For example, sprinters in a race may have achieved the same velocity at the finish, therefore,...
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Controlled nuclear fission reactions are used to generate electricity. Any nuclear reactor that produces power via the fission of uranium or plutonium by bombardment with neutrons has six components: nuclear fuel consisting of fissionable material, a nuclear moderator, a neutron source, control rods, reactor coolant, and a shield and containment system.
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Best (but oft forgotten) practices: sample size planning for powerful studies.

Samantha F Anderson1

  • 1Department of Psychology, Arizona State University, Tempe, AZ.

The American Journal of Clinical Nutrition
|May 28, 2019
PubMed
Summary

This guide explains statistical power and sample size planning for nutrition researchers. Proper planning enhances study reproducibility and scientific rigor.

Keywords:
designeffect sizemethodologysample sizestatistical power

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Area of Science:

  • Nutrition Science
  • Biostatistics

Background:

  • Concerns about scientific replicability and trustworthiness necessitate statistically rigorous research.
  • High statistical power is crucial for improving study quality and reproducibility.
  • Sample size planning is a key factor researchers can control to enhance statistical power.

Purpose of the Study:

  • To provide an accessible overview of statistical power and sample size planning.
  • To emphasize the importance of statistical power for high-quality scientific research.
  • To equip nutrition researchers with tools for effective sample size planning.

Main Methods:

  • Review of statistical power concepts.
  • Explanation of sample size planning methodologies.
  • Inclusion of practical examples relevant to nutrition research.

Main Results:

  • Detailed explanation of statistical power and its impact on research.
  • Illustrative examples of sample size planning for nutrition studies.
  • Discussion of practical challenges in sample size planning.

Conclusions:

  • Effective sample size planning is essential for robust and reproducible scientific findings.
  • Nutrition researchers can improve study quality by understanding and applying statistical power principles.
  • This article serves as a resource for conducting statistically sound future studies.