Introduction to Biostatistics and Epidemiology

Foundational vocabulary and concepts for Biostatistics and Epidemiology, covering Evidence Based Health Care, study designs, types of variables, and scales of measurement.

Evidence Based Health Care

Management of patients or clients using the current best evidence of effectiveness.

The 5 Steps of Evidence Based Health Care

1. Translate uncertainty into an answerable question; 2. Search & find best available evidence; 3. Critically appraise the evidence; 4. Apply the evidence in practice; 5. Evaluation.

3 Components of Evidence Based Healthcare

Patient Choice, Clinical Experience, and Best available evidence.

Epidemiology

The study of distribution and determinants of disease (or health-related states or events) in specified populations, and the application of this study to the control of health problems.

The 3Ds of Epidemiology

Disease, Distribution, and Determinants.

Case control studies

Epidemiological studies that compare diseased individuals with non-diseased individuals.

Cohort studies

Epidemiological studies that compare exposed individuals with non-exposed individuals in terms of their risk for a disease.

Biostatistics

Statistics related to health and biological fields involving collecting, summarising, analysing, and drawing conclusions from data.

Population

Total number of persons (or objects) from which data can potentially be drawn.

Sample

A subset of the population selected for a study.

Sampling frame

The group (or target population) from which a sample is selected.

Simple random sample

A sampling method where everyone has an equal chance (or probability) to be in the sample.

Population Parameter

An unknown population value.

Descriptive statistics

Statistics that describe the sample representing a population.

Inferential statistics

Statistics that infer or make conclusions about the impact of variables.

Independent Variable

The factor that is manipulated in an experiment.

Dependent Variable

The factor that is measured in an experiment (also known as the Outcome).

Extraneous variable

A variable that can affect the reliability of the study but can be controlled.

Confounding variable

A variable that can affect the reliability of the study but is difficult or cannot be controlled.

Exposure

A determinant or influencing factor that may be harmful or beneficial.

Sampling variation

Varying results observed from sample to sample.

Sampling error

The difference between an estimated value of a parameter and its real/true value.

Categorical variables (Qualitative data)

Variables on which individuals can be categorised according to some characteristic or quality, consisting of Nominal and Ordinal variables.

Continuous variables (Quantitative data)

Characteristics that are measureable, consisting of Interval and Ratio variables.

Nominal scale

Data that exists in name only with no meaningful relationship between categories and no info regarding magnitude (e.g., Binary variables like Dead/Alive).

Ordinal scale

Categories based on ranking or order where the relationship is preserved, but the gaps between categories are not numerically equal.

Interval scale

Data with numerically equal intervals between measurements but no true zero point (e.g., Temperature, IQ test scores).

Ratio scale

Data with numerically equal intervals and an absolute/true zero representing the complete absence of the characteristic (e.g., Money, Heart beat/min).

Scale variables

The collective term used in SPSS for Interval and Ratio variables.