Linked from

The 14 pages that link to Missing data, each with the reason it gives.

• Selection biasRelated: Whether values are missing at random determines when complete-case selection distorts results.

• Exploratory data analysisRelated: Missingness can distort plots and summaries or reveal how data were collected.

• Longitudinal studyRelated: Missed visits and dropouts create gaps that can distort estimated change.

• Nonresponse biasNarrower topic: Nonresponse is one source of missing data, with distinctive risks for inference.

• InterpolationRelated: Interpolation is sometimes used to fill gaps, though it can conceal uncertainty or real variation.

• Descriptive statisticsRelated: A summary may misrepresent observed data when missingness changes which cases are included.

• Time series analysisRelated: Gaps disrupt time spacing and can bias estimates or forecasts if ignored.

• BiostatisticsRelated: Missing observations can bias estimates unless their causes and structure are addressed.

• Data collectionRelated: Nonresponse and failed measurements leave gaps that collection plans can reduce or document.

• Expectation–maximization algorithmRelated: EM can estimate model parameters by integrating over unobserved values rather than discarding incomplete cases.

• Interquartile rangeRelated: Excluding or imputing missing values can change the ranks and resulting IQR.

• Line chartRelated: A missing observation raises the choice of whether to break the line or connect across the gap.

• Historical epidemiologyRelated: Incomplete archives can make disease rates appear lower or uneven across groups.

• Sample size determinationRelated: Expected missingness can reduce effective information and complicate enrollment targets.