Epidemiology & Biostatistics – Week 1 Vocabulary

40 vocabulary flashcards covering the key concepts, study designs, statistical terms, and measurement scales introduced in Week 1’s Epidemiology & Biostatistics lecture.

Evidence-Based Health Care (EBHC)

Managing patients using the current best evidence on effectiveness through five steps: ask, search, appraise, apply, evaluate.

Validity

The degree to which study results or measurements reflect the true, accurate value (truthfulness).

Reliability

Consistency or repeatability of results when a study or measurement is repeated under identical conditions.

Applicability

The extent to which evidence is relevant to a practitioner’s specific patient or population.

Statistical Inference

Drawing conclusions about a population based on information obtained from a sample.

Parametric Test

Statistical test that assumes data follow a specific distribution (usually normal) and are measured on interval/ratio scales.

Non-Parametric Test

Statistical test that makes no strict distributional assumptions; used with ordinal or non-normal data.

Epidemiology

Study of the distribution and determinants of disease or health-related events in specified populations for control of health problems.

Biostatistics

Collection, summarisation, and analysis of biological or health data to draw objective conclusions.

Population

Entire set of individuals or objects of interest from which data could be drawn.

Sample

Subset of a population selected for study, intended to represent that population.

Sampling Frame

Complete list of the target population from which a sample is drawn.

Population Parameter

Unknown true value that describes a characteristic of an entire population (e.g., mean blood pressure).

Descriptive Statistics

Numerical or graphical methods that summarise and describe features of a data set.

Inferential Statistics

Techniques that allow conclusions or hypotheses about relationships and differences within a population based on sample data.

Variable

Any characteristic or attribute that can vary among study subjects.

Independent Variable

Factor manipulated or considered as the cause/exposure in a study (predictor).

Dependent Variable

Outcome or response measured to assess the effect of the independent variable.

Extraneous Variable

A factor other than the independent variable that may influence study results but can be controlled.

Confounding Variable

An uncontrolled factor related to both exposure and outcome that can distort their true relationship.

Exposure

Determinant or factor to which individuals are subjected and whose effect is being studied.

Outcome

Health-related state or event being measured as the end-point of interest.

Sampling Variation

Natural differences in estimates that occur when different samples are drawn from the same population.

Sampling Error

Gap between a sample estimate and the true population value arising purely because a sample, not a census, is studied.

Categorical Data

Qualitative variables that place individuals into groups or categories (e.g., blood type).

Continuous Data

Quantitative variables that can take any numeric value within a range (e.g., weight).

Nominal Scale

Categorical measurement with names only and no inherent order (e.g., eye colour).

Binary Variable

Nominal variable with exactly two possible categories (e.g., smoker/non-smoker).

Ordinal Scale

Categorical measurement with a meaningful order but unequal intervals between categories (e.g., pain: mild/moderate/severe).

Interval Scale

Quantitative measurement with equal intervals but no true zero (e.g., temperature °C).

Ratio Scale

Quantitative measurement with equal intervals and a true zero allowing ratios (e.g., height, weight).

Case–Control Study

Observational study comparing individuals with a disease (cases) to those without (controls) to identify past exposures.

Cohort Study

Observational study following exposed and unexposed groups over time to compare incidence of outcomes.

Randomised Controlled Trial (RCT)

Experimental study allocating participants randomly to intervention or control to assess causal effects.

Bias

Systematic error that leads to an incorrect estimate of effect or association.

P-Value

Probability of observing results as extreme as those obtained, assuming the null hypothesis is true.

Confidence Interval (CI)

Range of values within which the true population parameter is expected to lie with a specified probability (e.g., 95%).

SPSS

Statistical software (Statistical Package for the Social Sciences) used for data entry, cleaning, and analysis.

Descriptive Graphs

Visual tools (e.g., bar chart, histogram, boxplot) used to summarise and display data distribution.

Evidence Appraisal

Systematic assessment of research to judge its trustworthiness, relevance, and value in a specific context.