Sample Selection
Sample Selection - Related Videos
06:08
A Novel Method: Super-selective Adrenal Venous Sampling
0 Views •
Cited by 44 •
2017
'Super-selective' adrenal venous sampling (ssAVS, also called segmental adrenal venous sampling: sAVS) was performed using a micro-catheter to identify adrenal segment(s) that produce excessive amounts of hormones. The ssAVS technique was described, cases in which adrenal segmental lesion(s) were identified by ssAVS were presented, and the usefulness of ssAVS in future adrenal research was discussed.
03:07
Selective Enrichment and Isolation of Oil-Degrading Marine Bacteria from Seawater Samples
0 Views •
2025
Source: Villela, H. D. M., et al. Prospecting Microbial Strains for Bioremediation and Probiotics Development for Metaorganism Research and Preservation. J. Vis. Exp. (2019)This video demonstrates the selective isolation of oil-degrading marine bacteria by enriching seawater samples on oil-based media, enabling the recovery of pure strains with potential applications in crude oil bioremediation and environmental cleanup.
09:13
Diagnostic Necropsy and Selected Tissue and Sample Collection in Rats and Mice
0 Views •
Cited by 49 •
2011
This article describes the procedures for conducting a basic postmortem examination of a mouse or rat, and the collection of basic organs, as well as more challenging sample types from for histological, microbiological, and PCR evaluation.
20:23
A Lectin HPLC Method to Enrich Selectively-glycosylated Peptides from Complex Biological Samples
0 Views •
Cited by 13 •
2009
Lectin-conjugated POROS beads were employed for HPLC. Glycopeptide standards served as positive and negative controls. MARS-14 depleted, trypsin-digested human plasma was chromatographed and flow-through (FT) and bound fractions collected for ESI-LC-MS/MS analyses. Glycopeptides were enriched in the bound fraction as compared to FT.
06:48
Natural Selection - Concepts
0 Views •
2019
Fitness Widespread variation of phenotypes in natural populations provides the raw material for evolution, which is the change in the inherited traits of populations over successive generations. Natural selection is one of the main mechanisms of evolution and requires variable traits to be heritable and associated with differential survival and/or reproductive success. Phenotypes that correlate with greater success will have more offspring that survive to reproduce in the next generation, and...
FAQs
The sampling frame determines which units can actually be chosen, so it must correspond closely to the defined target population. If the frame does not adequately represent that population, the resulting data may introduce sampling bias even when a probability method is applied. Reviewing this alignment supports more defensible generalization.
Probability sampling gives the selection process a statistical basis for reducing sampling bias and supporting conclusions about the wider population. Simple random, stratified, systematic, and cluster sampling are examples of probability approaches. Nonprobability sampling may be appropriate in some settings, but the choice should match the research question and intended interpretation.
Selecting among simple random, stratified, systematic, and cluster sampling requires considering how the design may affect representativeness, variability, and resource demands. These methods are not interchangeable in every study: the appropriate choice depends on the target population, the available sampling frame, and the research question. Design decisions therefore shape the quality and usefulness of the resulting data.
A sound workflow starts by specifying the target population and identifying the sampling frame. The researcher then chooses a probability or nonprobability approach and applies the selected design to obtain the subset of observations or measurements. The resulting data should be considered in light of possible sampling bias, variability, and the intended population-level conclusions.
Sample selection is central to statistical estimation and hypothesis testing because the observed subset supplies the data for those analyses. When selection limits bias and produces data that reflect the target population, estimates and tests can support conclusions beyond the sampled units. Poor selection can weaken that generalizability, regardless of the analysis performed.
The main practical value is efficiency: a study can use a subset rather than measure an entire population, reducing resource demands while still addressing a research question. In statistics, this makes sample selection relevant to estimation, hypothesis testing, and generalization. The design must balance efficient data collection with representative information and controlled sampling variability.