is the discipline that collects, organizes, analyzes, interprets, and presents data to infer meaningful information despite uncertainty.

Context: Definition and scope of statistics

A is the full set of people or objects about which conclusions are desired.

Context: Population vs sample

A is a subset used to make inferences that require representativeness.

Context: Population vs sample distinction

Representative sampling supports valid inference from sample to .

Context: Representativeness and inference target

Descriptive statistics vs inferential statistics: summarize data, while use sample data subject to random variation to draw conclusions about a population.

Context: Descriptive vs inferential statistics

Central tendency and dispersion: Central tendency describes typical values, while dispersion describes how values vary around the .

Context: Central tendency and dispersion

Hypothesis testing framework: The is an idealized baseline hypothesis (often “no relationship”) used as the starting point for testing.

Context: Hypothesis testing framework

Type I and Type II errors: error rejects the null hypothesis when it is actually true (false positive).

Context: Error types

Type I and Type II errors: error fails to reject the null hypothesis when it is actually false (false negative).

Context: Error types

Random vs systematic error: Measurement processes can produce random noise or systematic , and missing data or censoring can bias estimates if not handled.

Context: Random vs systematic error and missing/censoring

Cause to effect chain: Randomized assignment of treatments to subjects causes which leads to experimental error that is less biased by confounding.

Context: Design of experiments and causality

Cause to effect chain: Observation of participants (subjects know they are being studied) causes which can change outcomes even without the intended treatment effect.

Context: Hawthorne effect and experimental interpretation

Experimental vs observational: An is a study where the researcher manipulates the system and then measures outcomes to assess the effect of the manipulation.

Context: Experimental vs observational studies

Experimental vs observational: An is a study where data are collected without experimental manipulation, focusing on associations and correlations.

Context: Experimental vs observational studies

Levels of measurement: A is a scale with meaningful order but imprecise differences, allowing any order-preserving transformation.

Context: Nominal, ordinal, interval, ratio measurement scales