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Related Concept Videos

Sampling Plans01:23

Sampling Plans

1.2K
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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Ordinal Level of Measurement00:55

Ordinal Level of Measurement

36.4K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
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Stratified Sampling Method01:16

Stratified Sampling Method

16.0K
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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
16.0K
Bootstrapping01:24

Bootstrapping

904
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...
904
Frequency-dependent Selection01:21

Frequency-dependent Selection

24.4K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.4K
Cluster Sampling Method01:20

Cluster Sampling Method

15.4K
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...
15.4K

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SPCIS: Standardized Plant Community with Introduced Status database.

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A Method for Quantifying Foliage-Dwelling Arthropods
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Scale and Sampling Effects on Floristic Quality.

Greg Spyreas1

  • 1Illinois Natural History Survey, Champaign, IL, United States of America.

Plos One
|August 5, 2016
PubMed
Summary

Floristic Quality Assessment (FQA) metrics, like Mean C, are robust to sampling methods and year effects. This enhances FQA

Area of Science:

  • Ecology
  • Conservation Biology
  • Botanical Surveys

Background:

  • Floristic Quality Assessment (FQA) is vital for land management and conservation policy.
  • The ecological properties and limitations of FQA metrics, particularly concerning sampling methods and scale, are not fully understood.

Purpose of the Study:

  • To investigate FQA metric properties related to species detection, misidentification, sampling year, and plot size.
  • To assess the robustness of FQA metrics under varied sampling conditions.

Main Methods:

  • Utilized 12-year nested plot data from a tallgrass prairie remnant.
  • Analyzed FQA metrics (Mean C) against species detection rates, misidentification simulations, sample year, and plot grain/area.

Main Results:

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  • Plot size and species detection rates above 65% did not significantly affect Mean C.
  • Species misidentification impacted Mean C only when exceeding 10% in large plots with random species replacement.
  • FQA values demonstrated stability over the 12-year study period, showing no significant year effects.

Conclusions:

  • The FQA metric Mean C is robust to variations in sampling intensity (plot size, detection rate) and sample year.
  • Reduced sampling effort is needed for accurate FQA site assessments.
  • Consistent FQA metric performance allows for enhanced comparability across different sites and datasets.