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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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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. 
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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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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.
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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Related Experiment Video

Updated: Dec 1, 2025

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
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Surface hopping with cumulative probabilities: Even sampling and improved reproducibility.

Shane M Parker1, Colin J Schiltz1

  • 1Department of Chemistry, Case Western Reserve University, 10800 Euclid Ave., Cleveland, Ohio 44106, USA.

The Journal of Chemical Physics
|November 10, 2020
PubMed
Summary
This summary is machine-generated.

We introduce a new cumulative probability approach for fewest switches surface hopping (FSSH-c) in nonadiabatic molecular dynamics. This method enhances computational reproducibility and predictability in photochemical reaction simulations.

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Area of Science:

  • Computational Chemistry
  • Photochemistry
  • Molecular Dynamics

Background:

  • Trajectory surface hopping simulations are crucial for understanding photochemical reactions.
  • Current methods rely on statistical sampling, which can limit reproducibility and predictability.
  • Standard sampling is sensitive to numerical parameters like time step.

Purpose of the Study:

  • To develop a more robust and reproducible sampling strategy for surface hopping simulations.
  • To improve the predictability and time-step independence of trajectory surface hopping methods.
  • To address limitations in current statistical sampling approaches for nonadiabatic molecular dynamics.

Main Methods:

  • Developed a cumulative probability (FSSH-c) approach for fewest switches surface hopping (FSSH).
  • Implemented FSSH-c as a statistically equivalent alternative to instantaneous conditional probability (FSSH-i).
  • Proposed a scaling correction for conventional hopping probability overestimation.

Main Results:

  • FSSH-c offers improved computational reproducibility and predictability.
  • Hopping decisions in FSSH-c are independent of the time step.
  • Demonstrated advantages numerically on model scattering problems.
  • Identified and corrected overestimation in conventional hopping rates.

Conclusions:

  • The cumulative probability approach (FSSH-c) provides a more reliable method for simulating photochemical reactions.
  • FSSH-c enhances the robustness of nonadiabatic molecular dynamics by decoupling hopping decisions from time step.
  • This work enables exploration of convergence behavior and alternative sampling schemes.