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Related Concept Videos

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 Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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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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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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The X̄ Chart00:58

The X̄ Chart

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The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
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Bandpass Sampling01:17

Bandpass Sampling

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
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Updated: Apr 21, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Adaptive Multi-Wave Sampling for Efficient Chart Validation.

Georg Hahn1, Sebastian Schneeweiss1, Shirley V Wang1

  • 1Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Clinical Epidemiology
|April 20, 2026
PubMed
Summary
This summary is machine-generated.

Random sampling with Bayesian credible intervals efficiently validates outcomes in healthcare data. This method minimizes chart reviews, improving the characterization of patients and study findings.

Keywords:
Bayesian credible intervalsNeyman’s samplingchart reviewconfidence bandsrandom sampling

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Area of Science:

  • Health Informatics
  • Biostatistics
  • Clinical Research Methods

Background:

  • Healthcare data, including electronic health records and claims, are crucial for patient characterization and outcome identification.
  • Computable phenotypes require validation through chart review studies to assess their measurement characteristics, such as positive predictive value.

Purpose of the Study:

  • To develop and evaluate an adaptive method for validating computable phenotypes using healthcare data.
  • To minimize the number of charts required for review while ensuring accurate measurement characteristics.

Main Methods:

  • An adaptive sampling approach (Neyman's sampling) was compared against random and stratified random sampling.
  • Frequentist confidence bands and Bayesian credible intervals were proposed for sequential evaluation of the quantity of interest.
  • A tool was developed to predict the stopping time, i.e., the number of charts needed for review completion.

Main Results:

  • Bayesian credible intervals demonstrated tighter intervals compared to frequentist confidence bands.
  • Simple random sampling performed comparably to Neyman's sampling in efficiency.
  • The adaptive approach effectively minimizes the number of charts reviewed.

Conclusions:

  • Random sampling combined with Bayesian credible intervals offers an efficient strategy for validating outcomes in both binary and continuous data.
  • This approach enhances the reliability of computable phenotypes derived from healthcare data.