Missing data
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Read explanation on this page ↓ See diagram ↓Missing data — on-site reading
An introductory overview for this topic. 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.
In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data.
Missing data can occur because of nonresponse: no information is provided for one or more items or for a whole unit ("subject"). Some items are more likely to generate a nonresponse than others: for example items about private subjects such as income. Attrition is a type of missingness that can occur in longitudinal studies—for instance studying development where a measurement is repeated after a certain period of time. Missingness occurs when participants drop out before the test ends and one or more measurements are missing.
Data often are missing in research in economics, sociology, and political science because governments or private entities choose not to, or fail to, report critical statistics, or because the information is not available. Sometimes missing values are caused by the researcher—for example, when data collection is done improperly or mistakes are made in data entry.
These forms of missingness take different types, with different impacts on the validity of conclusions from research: Missing completely at random, missing at random, and missing not at random. Missing data can be handled similarly as censored data.
How this connects to Biostatistics and Data Science
Statistical inference links sample measurements to estimates under explicit assumptions. Effect size, precision, confounding, missing data and model validation matter more than a single threshold. Reproducibility requires documenting data transformations, analysis code and justified decision rules.
Text credit: Wikipedia contributors, “Missing data”, 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.
Missing data · 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)
All reading material on this page appears above. The links below are for checking the primary syllabus, research or source attribution, not requirements for opening this lesson.