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Related Concept Videos

Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Distributions to Estimate Population Parameter01:26

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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Sample Size Calculation01:19

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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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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.
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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.
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Sampling Soils in a Heterogeneous Research Plot
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Patchy distributions: Optimising sample size.

J E Hewitt1, G B McBride, R D Pridmore

  • 1Water Quality Centre, Ecosystems Division, National Institute of Water and Atmospheric Research, P.O. Box 11115, Hamilton, New Zealand.

Environmental Monitoring and Assessment
|November 14, 2013
PubMed
Summary
This summary is machine-generated.

A new sample size estimation method requires no prior precision definition or normality assumptions. Five modifications simplify its use and improve accuracy for environmental sampling programs.

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Area of Science:

  • Ecology
  • Environmental Science
  • Statistics

Background:

  • Traditional sample size estimation often requires predefined precision levels and assumes normal distribution.
  • These assumptions can be limiting in ecological studies with non-normal data, such as patchy distributions.

Purpose of the Study:

  • To discuss a recently proposed sample size estimation method.
  • To present five modifications enhancing the method's usability and accuracy.
  • To extend the method for estimating mean abundance in patchy environments.

Main Methods:

  • Discusses a novel sample size estimation technique.
  • Introduces five modifications to the existing method.
  • Applies the extended method to estimate mean abundance of benthic organisms.

Main Results:

  • The modified method is easier to use and reduces overestimation of sample size.
  • The technique is applicable to estimating mean abundance in patchy distributions.
  • Provides guidance for designing environmental sampling programs.

Conclusions:

  • The proposed method and its modifications offer a flexible approach to sample size estimation.
  • This technique is valuable for designing environmental surveys, particularly those involving comparisons across time or space.
  • It addresses limitations of traditional methods when dealing with non-normal population distributions.