Validity and reliability
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Read explanation on this page ↓ See diagram ↓Validity (statistics) — on-site reading
This reference extract addresses Validity (statistics), a related subject. It does not cover every part of Validity and reliability.. The article introduction is reproduced here, so you do not need to leave MedAtlas to read it. It may not match the latest official medical guidance.
Validity is the main extent to which a concept, conclusion, or measurement is well-founded and likely corresponds accurately to the real world. The word "valid" is derived from the Latin validus, meaning strong. The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.
In psychometrics, validity has a particular application known as test validity: "the degree to which evidence and theory support the interpretations of test scores" ("as entailed by proposed uses of tests").
It is generally accepted that the concept of scientific validity addresses the nature of reality in terms of statistical measures, and as such is an epistemological and philosophical issue as well as a question of measurement. The use of the term in logic is narrower, relating to the relationship between the premises and conclusion of an argument. In logic, validity refers to the property of an argument whereby if the premises are true then the truth of the conclusion follows by necessity. The conclusion of an argument is true if the argument is sound, which is to say if the argument is valid and its premises are true.
By contrast, "scientific or statistical validity" is not a deductive claim that is necessarily truth preserving, but is an inductive claim that remains true or false in an undecided manner. This is why "scientific or statistical validity" is a claim that is qualified as being either strong or weak in its nature, it is never necessary nor certainly true. This has the effect of making claims of "scientific or statistical validity" open to interpretation as to what, in fact, the facts of the matter mean.
Validity is important because it can help determine what types of tests to use, and help to ensure researchers are using methods that are not only ethical and cost-effective, but also those that truly measure the ideas or constructs in question.
How this connects to Research Methodology
The digestive and hepatobiliary systems coordinate motility, secretion, absorption, metabolic processing and transport through anatomically distinct compartments. Functional, inflammatory, obstructive, infectious and neoplastic mechanisms can produce overlapping symptoms; accurate localization and context are needed for interpretation.
Text credit: Wikipedia contributors, “Validity (statistics)”, original article · authors & revision history · CC BY-SA 4.0. Unmodified opening extract, accessed 24 September 2026. This Wikipedia-derived section is provided under CC BY-SA 4.0; the independent MedAtlas notes and design are separate works.
Validity and reliability · visual study map
Scalable vector illustration. Labeled conceptual map, not a precise anatomical, histological or diagnostic image.The wording in this learning map is adapted from the attributed Wikipedia background section below (CC BY-SA 4.0).
What the underlying subject studies
Biomedical research begins with a focused, ethically valid question. Define the study population, variables, measurements and decision-relevant uncertainty before selecting a method.
How mechanisms and evidence connect
Compare alternative study designs and account for sampling, confounding, bias, imprecision, missing data and the limits of causal inference. Sound statistical analysis cannot repair invalid measurements or unethical recruitment.
How to develop a sound explanation
Doctoral-level mastery involves reading primary methods, reproducing calculations, writing transparent protocols and defending the assumptions behind each conclusion. University-specific courses and laboratory competencies differ.
References and verification (optional)
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