Data Examination
Data Examination - Related Videos
07:44
Ophthalmoscopic Examination
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2023
Source: Richard Glickman-Simon, MD, Assistant Professor, Department of Public Health and Community Medicine, Tufts University School of Medicine, MA The simplest ophthalmoscopes consist of an aperture to look through, a diopter indicator, and a disc for selecting lenses. The ophthalmoscope is primarily used to examine the fundus, or the inner wall of the posterior eye, which consists of the choroid, retina, fovea, macula, optic disc, and retinal vessels (Figure 1). The spherical eyeball...
35:03
Mental Status Examination
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2023
Source: Carmen Black, Matthew Goldenberg, and Jaideep Talwalkar; Yale School of Medicine The mental status exam is a clinical evaluation of emotional, perceptual, and cognitive functioning that spans both the fields of psychiatry and neurology. Developing skill in the psychiatric portions of the examination are important to describe salient aspects of the patient's mental state that may be observed during routine history and/or physical exam. Proficiency in the cognitive portions of this...
20:06
The Comprehensive Infant Examination
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2023
Source: Heather Collette and Jaideep Talwalkar; Yale School of Medicine The newborn exam is important in establishing a baby's baseline health status as they enter the world. Newborn providers have the unique opportunity to evaluate a patient for the very first time. They are also tasked with identifying congenital anomalies that require early intervention to promote a long and healthy life for the child. Many newborn anomalies, such as heart defects, are noted on prenatal ultrasound. However,...
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...
FAQs
Completeness checks reveal whether important values are absent, while error checks identify entries that may be inconsistent with the intended dataset. These issues matter because missing or incorrect observations can distort summaries, obscure relationships, and lead analysts toward unsuitable statistical methods. Recording what was found and how it was handled also makes later interpretation more transparent and supports reproducibility.
An outlier should be treated as a signal for investigation rather than automatically deleted. Examination can show whether an extreme value reflects an error, an unusual but valid observation, or a feature of the data structure. Reviewing such values alongside the distribution and descriptive measures helps prevent misleading summaries while preserving information that may affect statistical conclusions.
Distributional shape and variability influence how results should be summarized and which formal analyses are appropriate. Averages alone may hide uneven spread or unusual values, whereas examining the distribution reveals how observations are arranged. In statistics, this comparison helps connect the observed data with methods that fit its characteristics and reduces the risk of drawing conclusions from an incomplete summary.
A practical examination begins by checking completeness and possible errors, then organizing the data so observations and variables can be reviewed consistently. Next, descriptive measures and visualizations are used to inspect distributions, variability, and relationships. Findings from these checks guide the choice of formal statistical methods and should be documented so the path from raw data to conclusions remains clear.
Descriptive measures condense data numerically, whereas visualizations display patterns that a single number may conceal. Using both allows analysts to compare central summaries with the observed distribution and variability, and to inspect possible relationships between variables. This combined view is useful when deciding whether a result appears representative of the data or may be shaped by errors or unusual observations.
Beyond research studies, data examination supports quality control, surveys, and evidence-based decision-making. In each setting, the purpose is to assess whether the available data can support a dependable interpretation before formal analysis or action. Clear examination records improve transparency by showing how data quality, structure, and statistical meaning were considered rather than treating reported results as self-evident.