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Cross-sectional studies may be either descriptive or analytical. Descriptive studies mostly aim to provide estimates of prevalence of disease, traits such as smoking behavior, people′s attitudes, knowledge or health behavior, whereas analytical studies aim to assess associations between different parameters.

Antecedent-consequent bias: occurs in cross- sectional studies when it cannot be determined if exposure preceded disease. Prevalence: the proportion of diseased individuals in a population.

One cross-sectional study example in medicine is a data collection of smoking habits and lung cancer incidence in a given population. A cross-sectional study like this cannot solely determine that smoking habits cause lung cancer, but it can suggest a relationship that merits further investigation.

Incidence‐prevalence bias. Incidence‐prevalence bias results from the inclusion of prevalent cases in a study. It is most common in cross‐sectional studies, but can also be found in cohort studies (Table ​ 1).

Recall bias and detection bias are two common information biases. Because exposure and outcome are measured simultaneously in a cross-sectional study, prior knowledge of the condition might influence the ascertainment of the exposure or the outcome, which results in recall bias.

For example, epidemiologists who are interested in the current prevalence of a disease in a certain subset of the population might use a cross-sectional design to gather and analyze the relevant data.