Chapter 1 (2024 -1)

Chapter 1: Introduction to Biostatistics

What is Statistics?

• Definition: A field of study focused on:

  • Collecting data

  • Organizing data

  • Summarizing data

  • Analyzing data

  • Drawing inferences from data.

Understanding Data

• Data: Numbers obtained from measurements or counting; serve as the raw material for statistics.

Types of Statistics

• Descriptive Statistics: Methods for organizing, presenting, and summarizing data.

• Inferential Statistics: Methods for making decisions about a population based on analysis of a sample.

Course Hierarchy

• Descriptive Statistics:

  • Chapter 1: Introduction to Biostatistics

  • Chapter 2: Descriptive Statistics

  • Chapter 15: Vital Statistics

  • Chapter 12: Chi-square Distribution

• Probability Theory:

  • Chapter 3: Basic Probability Concepts

  • Chapter 4: Probability Distributions

  • Chapter 5: Important Sampling Distributions

• Inferential Statistics:

  • Chapter 7: Hypothesis Testing

  • Chapter 9: Linear Regression and Correlation

  • Chapter 13: Nonparametric Statistics

Sources of Data

• Types of Sources:

  • Day-to-day logs of organizational transactions.

  • Surveys (questionnaires).

  • Experiments (medical strategies).

  • External sources (published reports, data banks).

Statistics vs. Biostatistics

• Statistics: Tools are applied across various fields.

• Biostatistics: Application of statistical tools in biological sciences and medicine.

Data vs. Variables

• Variable: Observable characteristic that varies among entities.

  • Quantitative: Measurable characteristics (e.g., heights).

  • Qualitative: Categorical characteristics (e.g., gender).

Random Variables

• Definition: A quantitative variable whose values arise by chance.

  • Discrete Random Variable: Has distinct gaps in possible values (e.g., daily admissions to a hospital).

  • Continuous Random Variable: No gaps in values (e.g., height).

Population vs. Sample

• Population: The largest collection of entities of interest at a given time.

• Sample: A subset of the population.

Why Use Samples?

• Cost and time efficiency compared to studying the whole population.

• Some variables may involve destructive measuring methods.

• Populations may be infinite.

Measurement and Measurement Scales

• Measurement: Assigning numbers to objects/events based on rules.

• Types of Measurement:

  • Qualitative: Nominal and Ordinal Scales.

  • Quantitative: Interval and Ratio Scales.

Nominal vs. Ordinal Scales

• Nominal Scale: Classification without rank (e.g., gender).

• Ordinal Scale: Classification with rank, e.g., pain levels.

Interval vs. Ratio Scales

• Interval Scale: Identifies order but has no true zero (e.g., temperature).

• Ratio Scale: Identifies order with a true zero (e.g., weight).

Statistical Inference

• Definition: Drawing conclusions about populations from sample information.

• Research Study: Scientific study involving design and data analysis.

• Experiments: Observations following specific manipulations.

Sampling Methods

• Sampling with Replacement vs. without Replacement: Methodologies affecting sample selection.

• Simple Random Sampling: Every member of the population has an equal chance of selection.

• Systematic Random Sampling: Selection based on a fixed interval from a randomly chosen start.

• Stratified Random Sampling: Population partitioned into strata; samples drawn from each stratum.

Importance of Accurate Measurement

• Accuracy and Validity: Correctness of measurement.

• Precision and Reliability: Consistency of measurement.

• Treatment Group: Exposed to treatment.

• Control Group: Not exposed to treatment.

Computers in Biostatistical Analysis

• Benefits:

  • Fast and accurate calculations.

  • Random number generation capabilities.

  • Software like MS Excel/MegaStat for data analysis.