Simple Random Sampling
Simple Random Sampling - Related Videos
01:09
Random Sampling Method
0 Views •
2023
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...
06:06
A Simple Dewar/Cryostat for Thermally Equilibrating Samples at Known Temperatures for Accurate Cryogenic Luminescence Measurements
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Cited by 1 •
2016
A simple liquid nitrogen Dewar/cryostat apparatus comprised of a small fused silica optical Dewar, a thermocouple, and a charge-coupled device (CCD) spectrograph are described. The experiments for which this Dewar/cryostat is designed require fast sample loading, freezing, and alignment, accurate and stable sample temperatures, and small size/portability.
11:14
Primer-Free Aptamer Selection Using A Random DNA Library
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Cited by 20 •
2010
SELEX protocols comprise multiple rounds of selection, each of which require regeneration of bound ligands, which in turn require fixed primer sequences flanking the random library regions. These fixed primer sequences can interfere with the selection process (false positives and negatives). Here we present a primer-free protocol.
08:59
The Simple Experiment: Two-group Design
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2023
Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco—Monmouth University A two-group design is the simplest way to establish a cause-effect relationship between two variables. This video demonstrates a simple experiment (two-group design). In providing an overview of how a researcher conducts a simple experiment (two-group design), this video shows viewers the process of turning ideas into testable ideas and forming hypothesis, the identification and effect of...
11:17
A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample
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Cited by 67 •
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A protocol for comprehensive extraction of lipids, metabolites and proteins from biological tissues using one sample is presented.
FAQs
The sampling frame determines which individuals can actually be selected, so its completeness is essential. If eligible members are missing, the random procedure cannot give them a chance of inclusion, even when the selection process is unbiased. Researchers therefore need to align the frame with the target population before drawing conclusions about the broader group.
Random selection prevents researchers from choosing participants according to visible characteristics, preferences, or convenience. A random-number generator or lottery procedure applies the selection rule without favoring particular individuals. This reduces systematic distortion in the sample, although it does not eliminate sampling error or correct problems caused by an incomplete sampling frame.
An adequate sample size supports more dependable estimates, while an inadequate sample may produce results that differ substantially from the population simply through chance. Simple Random Sampling allows researchers to quantify sampling error, making the uncertainty associated with an estimate part of the statistical analysis rather than treating the sample result as exact.
Researchers first specify the target population and assemble a sampling frame containing its eligible members. They then assign or otherwise identify each member within that frame and use a random-number generator, lottery procedure, or similar random process to select the sample. The selected individuals become the basis for the survey, experiment, or quality-control analysis.
A random-number generator and a lottery procedure are both suitable tools when they give eligible members the intended equal, known opportunity for selection. The important feature is not the particular tool but the absence of preferential choice. Documenting the selection process also helps show how participants entered the sample and supports evaluation of the study design.
This approach is useful in surveys, experiments, and quality-control studies when researchers need sample information to inform conclusions about a broader population. Its statistical value comes from combining an unbiased selection process with a suitable sampling frame and adequate sample size. Under those conditions, researchers can analyze estimates alongside their sampling error.
A properly selected sample can provide estimates that represent characteristics of the broader population, while sampling-error analysis indicates how much those estimates may vary because only a subset was observed. The conclusions remain conditional on the frame covering the target population and the sample being large enough to support the intended analysis.