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A case–control study selects subjects according to an outcome: cases have the outcome and controls represent an appropriate comparison population. It compares their prior exposures or genetic variants to study association. Its retrospective sampling must be accounted for when interpreting risks; selection bias and confounding can distort an unadjusted association. In genetics, matched family pseudo-control genotypes can replace unrelated controls for a conditional transmission disequilibrium test.

Hypercalcaemia is an abnormally elevated serum calcium concentration. A diagnosis in medical records depends on testing and diagnostic practice, which can introduce selection bias into an observational study.

An observational study measures exposures and outcomes without assigning the exposure by an intervention. Its causal interpretation therefore requires assumptions that address confounding and selection bias.

Two reasons are selection bias and unequal ascertainment.

A Type Ia supernova can be standardized as a standard candle by correcting its peak brightness using the light curve shape and colour index. Its host spectrum gives the redshift; the corrected apparent magnitude and calibrated absolute magnitude give the distance modulusThe resulting Hubble diagram measures the shape of the luminosity distance as a function of redshift. A spatially flat matter-and-vacuum model, for example, predictsNonzero cosmological spatial curvature changes both the expansion rate and the conversion from radial to transverse comoving distance. Comparing the observed Hubble diagram with these predictions constrains the matter density parameter, cosmological constant and cosmological spatial curvature; a single low-redshift observation cannot separate them. The nearby sample calibrates the common intercept, while distant Type Ia supernovae supply the redshift-dependent shape. Relative faintness at a specified redshift means a greater inferred luminosity distance.

Type Ia supernova cosmology requires correction for interstellar extinction, possible evolution of the standardized luminosity, and selection bias. The absolute magnitude–Hubble constant degeneracy means that an uncalibrated sample constrains the shape rather than independently determining $H_0$.

Use a longitudinal study with repeated measurements of the same individuals. Recruit people across several starting ages and measure the same ear, with a standardized anatomical definition and instrument, at baseline and subsequent visits over several years. Repeat measurements at each visit to estimate measurement error; where practical, conceal previous measurements from the observer. Record sex, body size, birth cohort and observer, and document reasons for loss to follow-up.

A simple random-intercept linear mixed model for ear length $Y_{ij}$ at elapsed follow-up time $t_{ij}$ isThe individual intercept ${\alpha }_i$ absorbs persistent differences between people. Alternatively, differences $Y_{ij}-Y_{i0}$ remove that intercept directly. Estimate the within-person regression coefficient $\beta$ and its confidence interval, accounting for the correlation of repeated measurements. Compare it with the cross-sectional slope rather than treating the two slopes as automatically identical.

This longitudinal study directly checks whether an individual's ears tend to lengthen as time passes. Several entry-age cohorts help assess whether the change varies with starting age. Calendar-period changes and selective dropout can still affect interpretation, so repeated observation alone does not eliminate every possible source of confounding or selection bias.

A cross-sectional age association need not equal within-person growth. The following alternatives each suggest a concrete extension of the study.

These explanations are hypotheses to be checked, not established facts about the participants. Neither a small p-value nor an approximately linear scatter plot excludes them.

The groups were not formed by randomization. Their background health, access to care, clinic, country and reasons for selecting a delivery method can predict both the method and the outcome, creating confounding and selection bias. Moreover, the label describing the reference delivery may be determined after labour: patients whose attempted nonoperative delivery develops a complication can move into an operative category, leaving a selected uncomplicated reference group. Comparing intended strategies would avoid treating this post-baseline classification as a baseline assignment.

A useful comparison would therefore align eligibility and follow-up time, compare intended delivery strategies among patients genuinely eligible for both, and account for sufficient pretreatment predictors. Logistic regression cannot create comparable patients where the positivity assumption fails or remove unmeasured confounding.

A complete-case analysis changes the mix of people contributing to the logistic regression. Let $A=1$ mean that both required variables are observed, and write $s_c(p,z)=\mathrm{Pr}(A=1\mid C=c,P=p,Z=z)$. Within $P>1$, Bayes' theorem givesIf completeness depends differently on the two responses, and that selection also varies with partner count or the other predictors, the extra term changes the fitted regression coefficients. For example, people with high counts and poor condom use can be under-represented, weakening or otherwise distorting the apparent association. Dropping people with missing $P$ also excludes some whose membership of the $P>1$ target group is unknown.

The fitted conditional association among complete responders need not equal the target-population association. No direction of selection bias follows without specifying the response mechanism. Selection on predictors alone need not bias a correctly specified conditional logistic regression; it is the outcome-related selection described here that removes that reassurance.

Treat the extra measurements as auxiliary variables for multiple imputation. Include alcohol consumption and disease history as predictors in both the $C$ and $P$ conditional distributions, together with age, sex and the other incomplete response. If these auxiliary variables themselves have missing entries, give them appropriate models and update them within the same multiple imputation by chained equations sweeps. Preserve observed entries throughout.

Useful auxiliary measurements can improve predictions, precision and the plausibility of missing at random. Their association with the missing responses reduces uncertainty in the imputed values; association with nonresponse may explain some selection that otherwise depends on unobserved values. This can reduce selection bias under the enlarged missing at random model and reduce between-imputation variance. It does not guarantee elimination of missing not at random dependence. They need not be added to the final substantive logistic regression merely because they help imputation: the final analysis should still estimate its intended association.

For the electronic-record observational study, three strengths are:

Three weaknesses are:

For two-sample Mendelian randomization, three strengths are:

Three weaknesses are:

Thus the agreement supports the hypothesis, but does not establish the effect of a dietary intervention.

The mediation estimate relies on several strong assumptions. Possible failures include mediator-outcome confounding, residual exposure-outcome or exposure-mediator confounding, and an exposure-induced mediator-outcome confounder. Smoking duration may itself be part of the causal pathway, making adjustment inappropriate. Self-reported cigarettes per day has measurement error and does not fully measure tobacco exposure. The case-control sampling can create selection bias; population stratification can confound the genotype relations; cancer may alter reported smoking; and the product-of-coefficients calculation can be inappropriate for a binary outcome because odds ratio effects are nonlinear and noncollapsible. Any of these can attenuate or distort the indirect effect.

A complete-case analysis estimates each success probability among clinic attenders. If attendance depends on the unobserved outcome even after conditioning on treatment, the observed success proportions differ systematically from those in the randomized groups. This outcome-dependent dropout causes selection bias and can bias their difference.

The administrative data include only encounters with $M=1$. Conditioning on the stop indicator selects on the colliderwhich opens the noncausal path $D\leftrightarrow U\to Y$ and creates collider bias. The equal entries estimate only the selected risks $\mathbb{P}(Y=1\mid D=d,M=1)$. They ignore racial differences in the probability of being stopped and therefore do not identify the total causal effect of race on violence. This is also selection bias, because encounters with $M=0$ never enter the dataset.

The birth-period comparison requires consistency, no interference between individuals, and a real discontinuity in sugar exposure at the end of rationing. Near the cutoff, potential diabetes outcomes must otherwise vary continuously with birth date: no simultaneous policy, nutritional, diagnostic, seasonal, or demographic discontinuity may affect them. Birth dates must not be manipulable around the cutoff, and survival and UK Biobank participation must not create differential selection bias. These assumptions make cutoff assignment locally exchangeable and give a regression discontinuity design an exclusion restriction in which birth period affects diabetes through sugar exposure.