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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

390
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...
390
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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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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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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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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A Suite of Tutorials for the WESTPA 2.0 Rare-Events Sampling Software [Article v2.0].

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This study provides tutorials for the Weighted Ensemble (WE) strategy using WESTPA software. These guides cover preparing, running, and analyzing WE simulations for rare events like protein folding and binding.

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Area of Science:

  • Computational Chemistry
  • Biophysics
  • Systems Biology

Background:

  • The Weighted Ensemble (WE) strategy is effective for rare event simulations in molecular dynamics.
  • WESTPA software facilitates WE simulations for processes like protein folding and binding.

Purpose of the Study:

  • To provide comprehensive tutorials for WESTPA software, covering basic and advanced Weighted Ensemble simulations.
  • To guide users on best practices for preparing, executing, and analyzing WE simulations for diverse applications.

Main Methods:

  • Two sets of tutorials are presented: basic and advanced.
  • Basic tutorials cover molecular association, host-guest binding, peptide sampling, and protein folding.
  • Advanced tutorials focus on WESTPA 2.0 features like binless schemes, adaptive binning, HDF5 data handling, rate estimation, Python API, and specialized plugins.

Main Results:

  • The tutorials demonstrate applications for atomistic and non-spatial models, including protein folding and membrane permeability.
  • Users will learn to apply WE simulations to complex processes and large datasets.
  • Best practices for efficient simulation and analysis are detailed.

Conclusions:

  • These tutorials enhance the usability of the WESTPA software for advanced rare event simulations.
  • The WESTPA 2.0 features enable more efficient simulation and analysis of complex biological and chemical processes.
  • The provided resources cater to users with prior experience in molecular dynamics or systems biology simulations.