Data Sampling
Data Sampling - Related Videos
11:42
Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
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Cited by 4 •
2017
This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.
07:44
TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
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Cited by 3 •
2020
We present an optimized tandem mass tag (TMT) labeling protocol that includes detailed information for each of the following steps: protein extraction, quantification, precipitation, digestion, labeling, submission to a proteomics facility, and data analyses.
01:30
Data Interpretation
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2026
Research papers often present findings in the form of numerical tables, statistical outputs, and graphical representations. However, numerical results alone do not convey meaning unless researchers examine and place them in context. Data interpretation is the systematic process through which researchers transform raw information into clear, meaningful insights. It begins only after researchers have collected data through experiments, surveys, or observational studies. In their unprocessed form,...
00:59
How Data are Classified: Numerical Data
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2023
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data. Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
05:49
Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking
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Cited by 2 •
2019
This method involves utilization of clinical diagnostic data for prostate cancer patients in order to guide sampling procedures, when biobanking tissue following radical prostatectomy. This overcomes issues with previously published methods around efficiency and availability of fresh tissue for a wider range of downstream applications.
FAQs
The target population identifies the group a study seeks to describe, while the sampling frame establishes the basis for selecting observations. If the frame does not adequately cover that population, some relevant units may be missed before selection occurs. Consequently, even a carefully applied procedure may produce less representative results.
Each procedure determines how observations enter the subset, so the choice can affect selection bias, representativeness, and the uncertainty attached to estimates. Simple random, systematic, stratified, and cluster sampling are distinct options rather than interchangeable labels. Selecting an appropriate procedure helps produce evidence suitable for estimation, hypothesis testing, or decision-making.
Sampling is preferable when studying every member of the population would require more time, cost, or data processing than the research purpose justifies. A well-designed subset can still support estimates, hypothesis tests, and decisions while reducing those burdens. The tradeoff is that sampling introduces uncertainty, because only part of the population was observed.
Inadequate coverage excludes parts of the target population from the sampling frame or selection process, while nonresponse leaves selected observations without usable participation. Both problems can reduce representativeness and distort conclusions, even when the intended design uses a recognized sampling method. Evaluating these risks is essential when interpreting estimates or decisions based on the sample.
Begin by specifying the target population and sampling frame. Next, select a suitable procedure, such as simple random, systematic, stratified, or cluster sampling, and draw the manageable subset. The resulting observations can then support estimates, hypothesis tests, or decisions. This sequence connects the study's intended population with the evidence ultimately analyzed.
Sampling supports surveys, experiments, quality control, public health studies, and market research. In each setting, researchers can examine a subset rather than process a complete population, making data collection and analysis more manageable. Its statistical value depends on how well the design limits selection bias, addresses coverage and nonresponse, and supports conclusions about the intended population.
An estimate calculated from a subset can differ from the value that would be obtained from the entire target population because the study observes only selected observations. Sampling uncertainty describes this limitation and can be quantified as part of the statistical design. It does not automatically imply a poor study, since reliability also depends on selection bias, coverage, and nonresponse.