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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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Sampling Methods: Overview01:06

Sampling Methods: Overview

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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. 
In analytical chemistry, the choice of...
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Sampling Methods: Sample Types01:18

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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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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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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Related Experiment Video

Updated: Sep 17, 2025

Establishing Single-Cell Based Co-Cultures in a Deterministic Manner with a Microfluidic Chip
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Establishing Single-Cell Based Co-Cultures in a Deterministic Manner with a Microfluidic Chip

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Integration of multiple coinflip devices for high-quality random sampling.

Brady Taylor1,2, J Darby Smith3, Shashank Misra3

  • 1Sandia National Laboratories, Albuquerque, NM, 87123, USA. btaylor@sandia.gov.

Scientific Reports
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

Generating high-quality random numbers is crucial for artificial intelligence and scientific computing. This study uses tunnel diodes as coinflip devices, developing a system to produce reliable random bitstreams for probabilistic computing applications.

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Area of Science:

  • Stochastic computing
  • Microelectronic device engineering
  • Applied physics

Background:

  • Artificial intelligence, scientific computing, and probabilistic computing rely on random sampling, necessitating vast quantities of random numbers.
  • Stochastic microelectronic devices, or coinflip devices, offer a potential solution for high-rate random bit generation but suffer from analog nonidealities like temperature dependence and drift.
  • These nonidealities can introduce determinism, compromising the quality of the generated random bitstreams.

Purpose of the Study:

  • To address the challenges of nonidealities in coinflip devices for generating high-quality random bitstreams.
  • To develop a system for producing reliable and unpredictable random bits suitable for probabilistic computing.
  • To demonstrate the practical application of these bitstreams in a Monte Carlo approximation.

Main Methods:

  • Exploration of coinflip devices, specifically tunnel diodes, for random bit generation.
  • Implementation of a control loop to mitigate temperature dependence and ensure fair bit generation from individual devices.
  • Parallel combination of bitstreams from multiple tunnel diodes to achieve fair and unpredictable outputs.

Main Results:

  • A control loop was successfully implemented to adapt to temperature variations in tunnel diode coinflip devices, yielding fair bitstreams.
  • Combining parallel bitstreams from individual tunnel diodes produced high-quality, fair, and unpredictable random bitstreams.
  • The generated bitstreams were validated for their suitability in probabilistic computing through a Monte Carlo approximation of pi.

Conclusions:

  • Systems of multiple coinflip devices, when properly managed, can overcome analog nonidealities to produce high-quality random bitstreams.
  • The developed method provides a viable approach for generating the large volumes of random numbers required by advanced computing paradigms.
  • The successful Monte Carlo approximation demonstrates the practical utility of these engineered random bitstreams.