Introduction to Biostatistics

Introduction

• Biostatistics is a branch of statistics applied to health sciences
• It provides tools for organizing, analyzing, and interpreting data effectively
• It supports clinical decisions, public health policies, and medical research outcomes
• Biostatistics plays a significant role in designing experiments, conducting clinical trials, and improving public health outcomes

What is Statistics?

• Statistics involves collection, organization, summarization, and analysis of data
• The goal of statistics is to draw meaningful inferences from sample data
• Data consists of numbers resulting from measurements or counts

Biostatistics

• Biostatistics is the application of statistical methods to biological, medical, or health-related studies
• For example, it assesses the effectiveness of a new drug or the correlation between smoking and lung cancer

Biostatistician

• A biostatistician applies statistical methods to medical data in a research setting
• Roles include designing research studies, analyzing data for trends, and interpreting results for sound conclusions

Why Study Biostatistics?

• Biostatistics is the foundation of evidence-based medicine, enabling medical professionals to provide the best possible care
• Numerical data is essential in healthcare for making evidence-based decisions and improving patient outcomes
• Biostatistics is widely applied in clinical trials, epidemiology, patient care, public health, and medical research

Uses of Biostatistics

• Biostatistics is used to evaluate treatment effectiveness, monitor disease outbreaks and improve patient care
• Biostatistics helps professionals make accurate diagnoses, develop effective treatments and contribute to public health initiatives
• Examples include clinical trials, vaccination programs, and public health policies

Who Uses Statistics and Biostatistics?

• Physicians use statistics and biostatistics to evaluate treatment effectiveness, monitor patient outcomes, and make evidence-based decisions
• Epidemiologists use it to study disease patterns, outbreaks, and public health trends
• Healthcare administrators use it to allocate resources efficiently and improve operational performance
• Pharmacologists use it to assess drug efficacy and safety through bio-statistical analysis
• Biostatistics plays a critical role in modern medicine, ensuring that decisions and policies are based on reliable and scientific evidence

Types of Statistics

• Descriptive Statistics: Statistics focused on organizing, summarizing, and presenting data in a clear and understandable way

Descriptive Statistics

• Organize data into frequency distributions (such as frequency tables)
• Present data with various types of graphs such as bar charts, histograms, pie charts, and box plots
• Calculate important measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation)

Inferential Statistics

• Inferential statistics uses data from a sample to make conclusions or decisions about a larger population
• It also makes predictions, estimates, and generalizations about a population based on sample data, and evaluates confidence in those results

Measurement Scales

• Nominal scale: Lowest measurement scale used for classifying observations into mutually exclusive categories with no order or distance (e.g., gender, eye color)
• Ordinal scale: Observations are ranked and ordered, but the differences between categories are not necessarily equal, and distances are unequal (e.g., pain severity)
• Interval scale: Allows for the ordering of measurements with equal distances between two measurements, but the zero point is arbitrary (e.g., temperature in °C or °F, time in clock hours)
• Ratio scale: The highest level of measurement with equal distances and a true zero point (e.g., weight, height, age, income)

Sample and Population

• Sample: A subset of a population, used for practical measurement
• Population: The entire set of entities of interest in studying

Variables

• Variable: A characteristic that can take on different values

Types of Variables

• Quantitative: Measurable
• Qualitative: Categorical

Quantitative Variables

• Discrete: Takes distinct, separate values (e.g., number of patients in a hospital)
• Continuous: Takes any value within a range (e.g., weight, height)

Sources of Data

• Routinely kept records (e.g., hospital data)
• Surveys (when routine data is unavailable)
• Experiments (for controlled hypotheses)
• External sources (published reports, data banks)