Quantitative data

Quantitative data expresses observations as numbers that can be measured, counted, or analyzed statistically. It can describe amounts, counts, or numerical scores.

Measurement: The systematic assignment of values to properties according to defined rules. Quantitative data arise when measurement assigns numerical values to observed properties.
Data collection: The systematic gathering of observations for analysis or recordkeeping. Collection procedures determine which numerical observations enter a dataset.
Clinical trial: A study in which interventions are assigned to people to evaluate health effects. Trials compare numerical outcomes such as survival, blood pressure, or symptom scores.
Qualitative data: Information expressed through categories, descriptions, or other non-numerical forms. It contrasts with quantitative data by representing qualities rather than measured amounts.
Statistical significance: A result is statistically significant when it meets a specified threshold under a statistical test's assumptions. Numerical evidence can cross a significance threshold without implying a large or useful effect.
Variable (statistics): A characteristic that can take different values across observations. Quantitative data record numerical values of variables across cases.
Operationalization: The process of defining how an abstract concept will be observed or measured. It turns ideas such as stress or ability into measurable quantities.
Census: A systematic enumeration of a population, usually conducted by a government. Censuses produce counts and measured characteristics for entire populations.
Categorical variable: A variable whose values identify groups or categories rather than numerical amounts. Numeric codes for categories do not necessarily make the data quantitative.
Effect size: A quantitative measure of the magnitude of a difference, association, or intervention effect. Effect sizes show how substantial a pattern is beyond whether it is statistically detectable.

Linked from 8 pages