Cross-sectional studies are conducted at one time point or over a short period and are used to estimate the prevalence of an outcome in a population, often for public health planning. They provide a snapshot of the outcome and associated characteristics at a specific time. These studies are descriptive, often in the form of surveys, and are used to describe a population or subgroup with respect to an outcome and risk factors. They can also investigate associations between risk factors and outcomes, though they cannot infer causality due to the single time point. Repeated cross-sectional studies may provide a pseudo-longitudinal view, such as the British Association for the Study of Community Dentistry Survey, which tracks caries prevalence over time. Sample selection and response rates are crucial for generalizing results. A high response rate and representative sample are necessary to avoid bias. Nonresponse and biased response can distort results, especially if the characteristic being studied is related to the outcome. Cross-sectional studies can collect information on many outcomes and risk factors, but they are limited in establishing long-term associations. They are relatively inexpensive and time-efficient, useful for public health planning, understanding disease aetiology, and generating hypotheses. However, they are limited in making causal inferences and may suffer from prevalence-incidence bias, especially in long-lasting diseases. Recommended readings include Bland's "An Introduction to Medical Statistics" and the BASCD guidance on sampling for child dental health surveys.Cross-sectional studies are conducted at one time point or over a short period and are used to estimate the prevalence of an outcome in a population, often for public health planning. They provide a snapshot of the outcome and associated characteristics at a specific time. These studies are descriptive, often in the form of surveys, and are used to describe a population or subgroup with respect to an outcome and risk factors. They can also investigate associations between risk factors and outcomes, though they cannot infer causality due to the single time point. Repeated cross-sectional studies may provide a pseudo-longitudinal view, such as the British Association for the Study of Community Dentistry Survey, which tracks caries prevalence over time. Sample selection and response rates are crucial for generalizing results. A high response rate and representative sample are necessary to avoid bias. Nonresponse and biased response can distort results, especially if the characteristic being studied is related to the outcome. Cross-sectional studies can collect information on many outcomes and risk factors, but they are limited in establishing long-term associations. They are relatively inexpensive and time-efficient, useful for public health planning, understanding disease aetiology, and generating hypotheses. However, they are limited in making causal inferences and may suffer from prevalence-incidence bias, especially in long-lasting diseases. Recommended readings include Bland's "An Introduction to Medical Statistics" and the BASCD guidance on sampling for child dental health surveys.