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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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Random Sampling Method01:09

Random 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. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Bootstrapping01:24

Bootstrapping

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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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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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A Noninvasive Hair Sampling Technique to Obtain High Quality DNA from Elusive Small Mammals
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Sampling to Detect Rare Species.

Roger H Green, Richard C Young

    Ecological Applications : a Publication of the Ecological Society of America
    |May 1, 1993
    PubMed
    Summary
    This summary is machine-generated.

    Determining necessary sampling effort for rare species detection is crucial. A simple Poisson distribution model is generally adequate for estimating sample sizes, even for rare species, simplifying ecological survey planning.

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    Area of Science:

    • Ecology
    • Conservation Biology
    • Statistical Ecology

    Background:

    • Ecological sampling often aims to detect species presence, especially for rare or endangered species.
    • Accurate estimation of sampling effort is vital for efficient biodiversity assessments.
    • Spatial distribution patterns influence the required sample size for species detection.

    Purpose of the Study:

    • To evaluate the appropriateness of a Poisson distribution-based formula for estimating necessary sample size to detect species presence.
    • To determine if a simplified Poisson model is adequate compared to a more complex negative binomial model for rare species detection.

    Main Methods:

    • Analyzed published sampling data for 37 unionid mollusc species across river miles in two Appalachian rivers.
    • Estimated mean density, variance, and negative binomial parameter k for 273 species x river mile combinations.
    • Compared sample size estimates derived from Poisson and negative binomial distribution models.

    Main Results:

    • The Poisson-based formula provided adequate sample size estimates (within 5% of negative binomial estimates) in most cases (265 out of 273).
    • Only 8 cases involving rare species failed the 'Poisson adequacy' requirement (>0.95 proportion).
    • The Poisson distribution is generally suitable for rare species detection when mean density is low and aggregation is not extreme.

    Conclusions:

    • A Poisson-based approach for estimating necessary sample size is generally adequate and appropriate for detecting species presence, including rare species.
    • This simplification can streamline ecological survey design and resource allocation.
    • The study supports the use of the Poisson model for practical biodiversity monitoring.