Understanding Biostatistics

What is Statistics?

• Biostatistics is a science involved with understanding statistics (data) and the specific numbers or figures presented regardless of location.
• Biostatistics is involved with conducting studies to collect, organize, summarize and analyze data, and to draw conclusions or inferences
• Biostatistics is concerned with advancing all branches of science through evidence-based research
• Biostatistics is concerned with helping researchers, managers, and decision-makers to make decisions based on evidence

The Nature of Biostatistics

• Biostatistics is the term used when analyzing data derived from biological sciences and medicine
• Statistics are applicable in various fields including healthcare, medicine, nursing, public health, medical disciplines, education, economy, business, agriculture, sociology, administration, science and research.
• In healthcare and medicine, statistics are used for keeping records of diseases, patients, vitality, morbidity, and mortality
• Statistics are used to measure method of education, innovation, in education
• Statistics are used to measure economics, business, agriculture, sociology, administration, science, research in all fields

Data and Variables

• Data are values or measurement that indicate variables
• Data is the information collected, where the group of information forms a data set, and each value is a data point or datum
• Data is the raw material of statistics and data is considered to be figures
• Counting processes provide figures
• Measurements provide figures

Sources of Data

• Data sources include Routinely kept records, External sources, Surveys and Experiments
• Routinely kept records can include hospital medical records, which contain immense amounts of information on patients, and hospital accounting records, which contain a wealth of data on the facility's business activities.
• External sources are data that already exists in published reports, commercially available data banks or research literature
• A survey can be used as a data source if the data needed is about answering certain questions such as a survey to collect mode of transport information
• An experiment can be used as a data source if the data needed is only available as the result of an experiment such as if a nurse wishes to know which of several strategies is best for maximizing patient compliance.

Variables Defined

• A variable is a characteristic or attribute that can assume differing values in different persons, places, or things
• Variables whose values are determined by chance are called random variables.
• Examples of variables include heart rate, the heights of adult males, the weights of preschool children and the ages of a population

Types of Variables

• There are two types of variables: Quantitative and Qualitative Variables
• Quantitative variables are numerical
• Quantitative variables may be measured, ranked, assessed across samples or populations
• Types of Quantitative variables include Continuous and Discrete
• Continuous variables can assume any value within a specified relevant interval of values assumed by the variable, with fractions
• Heights, weights, and levels of glucose or cholesterol are examples of continuous variables
• Discrete variables are characterized by gaps or interruptions in the values that it can assume and have no fractions, and are countable
• Number of daily admissions to a hospital, and the number of children are examples of discrete variables
• Qualitative variables can be either Nominal or Ordinal
• Nominal variables classify data into mutually exclusive and nonoverlapping, exhaustive categories, and the variables cannot have any order or ranking imposed
• Gender, zip codes, political affiliation, and religion are examples of nominal variables
• Ordinal variables classify data into categories and feature ranking, however precise differences between ranks do not exist
• Letter Grades, judging contests, and ratings are examples of ordinal variables

Dependent vs Independent Variables

• Dependent variables (or response variables) are variables of primary interest, such as blood pressure in an antihypertensive drug trial, and they are not controlled by the experimenter
• Independent variables (or predictor variables) are called a factor when controlled by the experimenter and are often nominal, such as treatment.

Two Branches of Statistics

• Descriptive Statistics involves the collection, organization, summarization, and presentation of data
• Inferential Statistics involves generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions

Reasons to study biostatistics

• Medicine is becoming increasingly quantitative
• The planning, conduct, and interpretation of much medical research are becoming increasingly reliant on the statistical methodology

Roles of biostatistics in health

• Patient care
• Preventative medicine
• Health policies
• Health promotion
• Health planning and evaluation

Populations and Samples

• A population is the complete set of individuals, objects, or scores of interest
• A population encompasses all elements to be studied
• A population is often too large to sample in its entirety
• A population may be real or hypothetical
• A sample is a set or sub-collection of elements drawn from a population
• A sample may be classified as random, where each member has an equal chance of being selected from a population, or based on convenience of what is available
• Random selection attempts to ensure the sample is representative of the population