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Related Concept Videos

Sampling Plans01:23

Sampling Plans

809
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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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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Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
114
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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Experimental Designs01:16

Experimental Designs

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An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
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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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Updated: Dec 23, 2025

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Coverage-Based Designs Improve Sample Mining and Hyperparameter Optimization.

Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J Thiagarajan

    IEEE Transactions on Neural Networks and Learning Systems
    |April 20, 2020
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    Summary
    This summary is machine-generated.

    This study introduces coverage-based sampling for machine learning (ML), outperforming traditional methods in exploration and optimization tasks like hyperparameter tuning.

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    Area of Science:

    • Machine Learning
    • Optimization
    • Data Science

    Background:

    • Sequential optimization is crucial for ML tasks like data summarization and hyperparameter tuning.
    • Current methods often rely on discrepancy-based sampling, with initial sample quality being critical.
    • Coverage-based designs, like Poisson disk sampling, show promise from computer graphics but require adaptation for ML.

    Purpose of the Study:

    • To adapt coverage-based sampling designs for machine learning applications.
    • To develop a parameterized family of designs with provably improved coverage.
    • To create algorithms for effective sample synthesis in ML contexts.

    Main Methods:

    • Developed a parameterized family of coverage-based sampling designs.
    • Created algorithms for synthesizing samples based on these designs.
    • Evaluated the approach in sample mining and hyperparameter optimization for supervised learning.

    Main Results:

    • The proposed coverage-based approach consistently outperformed existing exploratory sampling methods.
    • Demonstrated superior performance in both blind exploration and Bayesian optimization.
    • Showcased improved coverage characteristics compared to traditional methods.

    Conclusions:

    • Coverage-based sampling offers a superior alternative to discrepancy-based methods for ML.
    • The developed methods enhance sample mining and hyperparameter optimization effectiveness.
    • Fundamental advances enable the successful adoption of coverage-based designs in ML.