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Stratified Sampling Method01:16

Stratified Sampling Method

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...
Random Sampling Method01:09

Random Sampling Method

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...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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...
Bootstrapping01:24

Bootstrapping

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 small or...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

Updated: May 20, 2026

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

Estimating species richness using the jackknife procedure.

J F Heltshe, N E Forrester

    Biometrics
    |March 1, 1983
    PubMed
    Summary
    This summary is machine-generated.

    This study provides an exact formula for the jackknife estimate of species richness and its variance using quadrat sampling. Simulations explored how quadrat size and sampling area affect these species estimation methods.

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    Last Updated: May 20, 2026

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    Area of Science:

    • Ecology
    • Statistical Ecology

    Background:

    • Estimating species richness is crucial for ecological studies.
    • Quadrat sampling is a common method for biodiversity assessment.

    Purpose of the Study:

    • To derive an exact expression for the jackknife estimator of species richness.
    • To develop an exact expression for the variance of the jackknife species richness estimator.
    • To investigate the influence of sampling parameters on the jackknife estimator.

    Main Methods:

    • The jackknife estimation method was applied to quadrat sampling data.
    • Exact formulas for the species richness estimate and its variance were derived.
    • Simulations were used to analyze the effects of quadrat size, sample size, and sampling area.

    Main Results:

    • An exact formula for the jackknife estimate of species richness was determined.
    • An exact formula for the variance of the jackknife estimate was constructed.
    • Approximate two-sided confidence intervals were developed.
    • Simulation results showed how sampling parameters impact the jackknife estimator's behavior.

    Conclusions:

    • The study provides a statistically rigorous method for estimating species richness using quadrat sampling.
    • The derived formulas and confidence intervals enhance the reliability of biodiversity assessments.
    • Understanding the impact of sampling design on estimators is vital for accurate ecological inference.