Introduction to Biostatistics: Data Collection, Processing & Sampling

Vocabulary flashcards summarizing the main terms and concepts from the lecture on biostatistics, including data collection, processing, and sampling methods.

Biostatistics

A branch of statistics that applies statistical methods to biological and health-related problems.

Biological Variability

The natural variation observed among living organisms that biostatistics seeks to measure and account for.

Quantitative Reasoning in Biology

Transforming qualitative biological observations into numerical data for objective analysis.

Statistical Inference

Drawing conclusions about a population based on data collected from a sample, including estimation and hypothesis testing.

Study Design

Planning experiments, clinical trials, or observational studies to collect reliable, relevant data (e.g., determining sample size, randomization).

Interdisciplinary Application

Collaboration of biostatistics with medicine, public health, genetics, epidemiology, pharmacology, and environmental science.

Clinical Trial

A research study in which human participants are prospectively assigned to interventions to evaluate health outcomes, often designed with biostatistical input.

Public Health Surveillance

Ongoing systematic collection, analysis, and interpretation of health data (e.g., infectious disease trends).

Genetic Research

The study of genes and heredity where statistical models identify disease-associated genes or inheritance patterns.

Data Collection

Systematic gathering of information or observations from a defined source.

Survey / Questionnaire

A structured set of questions administered to individuals to collect data, such as patient satisfaction or health behaviors.

Observation (Method)

Directly watching and recording phenomena, e.g., monitoring patient vital signs.

Experiment

A study in which variables are manipulated to observe effects on other variables (e.g., clinical trials with placebo control).

Existing Records / Databases

Previously gathered data sources like hospital records or national registries used for secondary analyses.

Interview

Structured or semi-structured conversation conducted to gather detailed information from participants.

Biometric Data

Physiological measurements such as blood pressure, weight, or genetic markers collected for analysis.

Data Processing

Operations performed on raw data to convert it into a usable and meaningful format for analysis.

Data Coding

Assigning numerical codes to qualitative information (e.g., male=1, female=0).

Data Entry

Inputting collected data into a computer system or database.

Data Storage

Securely saving processed data so it can be accessed and analyzed later.

Data Cleaning

Identifying and correcting or removing inaccurate, incomplete, or unreasonable data values.

Handling Missing Values

Techniques such as imputation or case exclusion to address absent data points.

Sampling

Selecting a subset of individuals from a population to draw conclusions about the whole.

Population (Target Population)

Entire group of individuals or objects that a researcher intends to study.

Sample

A subset of the population actually observed or measured.

Sampling Frame

A list or operational definition of all population units from which a sample is drawn.

Sampling Bias

Systematic error that occurs when some population members are more likely to be selected than others, yielding a non-representative sample.

Probability Sampling

Sampling method where every population member has a known, non-zero chance of selection (enables error estimation).

Simple Random Sampling

Every population member has an equal chance of being selected, e.g., random number generator.

Systematic Sampling

Selecting every k-th element from a sampling frame after a random start.

Stratified Random Sampling

Dividing the population into homogeneous strata and sampling randomly within each to ensure subgroup representation.

Cluster Sampling

Randomly selecting entire groups or clusters (e.g., villages) and studying all individuals within them.

Non-Probability Sampling

Sampling where selection chances are unknown, often convenience-based and prone to bias.

Convenience Sampling

Choosing easily accessible participants, e.g., clinic visitors, without randomization.

Purposive (Judgmental) Sampling

Hand-picking participants believed to be especially knowledgeable or relevant.

Quota Sampling

Non-random selection of participants within strata until predetermined numbers (quotas) are met.

Snowball Sampling

Existing participants recruit future participants, useful for hard-to-reach populations.