Biostat100A Lecture1

Biostat 100A

Introduction to Biostatistics
Dr. Karabi Nandy, [e-mail]

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Biostat 100A

Course

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Outline

Introduction
Descriptive statistics
Probability and sampling
Introduction to Statistical Inference
*A

detailed outline is available on course syllabus.

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Lecture 1: Descriptive Statistics
Introduction (Chapter 1.1 and 2.1):
1. Population and parameters
2. Sample and statistics
Nature of Data
3. Scales and measurement
Collecting and Entering Data (Chapter 3)
4. Data Entry
5. Data Cleaning and Management
6. Maintaining Proper Codebook

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Definition: Statistics
• Numerical properties of groups rather than individuals.
• Collection of methods that enables one to draw reasonable conclusions from data (Dunn, 1977).
• The art of making numerical conjecture about puzzling questions (Freedman,
1980).
• Biostatistics is the use of statistics, applied to biological problems and to medicine. 4

Two major branches of modern statistics
1. Descriptive – deals with techniques for summarizing and presenting data and is aimed at clarity (common understanding).
2. Inferential – deals with methods for assessing uncertainties in inductive estimates and is aimed at drawing conclusions from data (inference).

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1. Population and parameters
• Population – collection of objects or persons with one or more characteristics (variables) in common. “The set of things we wish to measure.” • Finite / Infinite
• Parameter – a characteristic of population under study.
• Ex: average, range
• Greek symbols ( ,

, )
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2. Sample and statistics
• Sample – subset of a population. “The actual set of things we measure.”
• Statistic – a characteristic of the sample collected in a study.
• Ex: average, range
• Roman symbols (x, s, r)

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Objective – make statement about population based on information in sample
Population
of units 2. Inference

1. Select

Sample of units
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Lecture 1: Descriptive Statistics
Introduction (Chapter 1.1 and 2.1):
1. Population and parameters