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Related Concept Videos

Sampling Plans01:23

Sampling Plans

221
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...
221
Stratified Sampling Method01:16

Stratified Sampling Method

12.1K
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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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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
Convenience Sampling Method00:55

Convenience Sampling Method

9.0K
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.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
9.0K
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
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...
12.0K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

303
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...
303

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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Tailorable Sampling for Progressive Visual Analytics.

Marius Hografer, Hans-Jorg Schulz

    IEEE Transactions on Visualization and Computer Graphics
    |May 19, 2023
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    Summary
    This summary is machine-generated.

    Progressive visual analytics (PVA) enables analysts to maintain workflow with evolving data insights. This study introduces a new PVA-sampling pipeline to adapt data partitioning without restarting computations, preserving analytical flow.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Progressive visual analytics (PVA) aids analysts by showing intermediate results for long computations.
    • Sampling methods are crucial for PVA to ensure early visualizations are useful for specific tasks.
    • Current PVA methods require restarting computations to change sampling strategies, disrupting analytical flow.

    Purpose of the Study:

    • To propose a novel PVA-sampling pipeline that allows dynamic adaptation of data partitioning.
    • To enable switching sampling methods on-the-fly without interrupting the analysis flow.
    • To enhance the flexibility and efficiency of progressive visual analytics.

    Main Methods:

    • Characterization of the PVA-sampling problem.
    • Formalization of a modular pipeline using specific data structures.
    • Development of on-the-fly tailoring capabilities for sampling strategies.

    Main Results:

    • A new pipeline for PVA-sampling is presented, allowing module switching without restarting analysis.
    • The proposed method maintains analytical flow by adapting to changing analysis needs.
    • Demonstrated usefulness through additional examples showcasing adaptability.

    Conclusions:

    • The developed PVA-sampling pipeline overcomes the limitation of fixed sampling strategies.
    • Enables seamless adaptation to evolving analysis tasks, preserving user flow.
    • Offers a more flexible and efficient approach to progressive visual analytics.