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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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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.
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...
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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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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.
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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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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Population Growth

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Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
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An open-population hierarchical distance sampling model.

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    Area of Science:

    • Ecology
    • Wildlife Biology
    • Population Dynamics

    Background:

    • Accurate wildlife population monitoring requires accounting for imperfect detection.
    • Distance sampling estimates population size but not demographic parameters.
    • Existing methods lack the ability to estimate demographic parameters from repeated surveys.

    Purpose of the Study:

    • To develop a novel model for estimating demographic parameters from repeated distance sampling surveys.
    • To assess the power of this model in detecting population trends using a simulation study.
    • To provide a framework for enhanced wildlife population monitoring.

    Main Methods:

    • Developed a model utilizing temporal correlation in abundance from underlying population dynamics.
    • Conducted a simulation study with temporally autocorrelated abundance and distance sampling data over six surveys.
    • Compared a data-generating Markovian model against mis-specified models and post hoc trend estimates.

    Main Results:

    • The developed Markov model demonstrated consistently greater power in detecting population changes compared to alternative models.
    • Model performance was particularly strong even with a reduced number of survey points.
    • The simulation, motivated by Island Scrub-Jay monitoring, highlighted the model's effectiveness.

    Conclusions:

    • The novel framework effectively estimates demographic parameters and detects population trends from distance sampling data.
    • The model offers a significant advancement for wildlife population monitoring programs.
    • The framework is adaptable for more complex demographic processes.