BIOSTATISTICS LESSON 1 INTRODUCTION-1.pdf

Mr. Abdiweli Mohamed Abdi
.
Biostatistics

Course contents
Biostatistics I
Chapter 1: Introduction to Biostatistics
Chapter 2: Data collection
Chapter 3; Organization of data
Chapter 4; Measures of location
Chapter 5; Measures of dispersion
Chapter 6; Visualization, presentation and interpretation of data
Chapter 7; Probability and Normal distribution of data
Chapter 8; Sampling techniques and Sample size calculation

CHAPTER ONE
Introduction to Biostatistics

Main themes
 Statistics vs Biostatistics
 Descriptive statistics vs Inferential statistics
 Statistical variables
 Statistical Scales of measurement
 Population vs Sample
 Common statistical symbols

Statistics – is a branch of
mathematics used for
collection, organizing, analysis
and interpretation of data.
Biostatistics- is a branch of
statistics used for collection,
organizing, analysis and
interpretation of biological
data.
Statistics vs Biostatistics
Thursday, January 23, 2025 5

Statistics
Use in Health Issues
Biostatistics
Use in Agricultural
Sector Agri-statistics
Use in Business Admin
Business Statistics
Use in Industrial
Sector Industrial
Statistics
Use in Insurance
Actuarial Statistics
Use in Economic Sector
Economic Statistics

Biostatistics Types
Inferential
 Measures of location
 measures of central tendency
 measures of other location
 Measures of Dispersion
 range
 variance
 standard deviation
 coefficient of variation
 Estimation
Point Estimation
Interval Estimation
 Hypothesis testing
 z-test  t test
 Anova   2 test
 Correlation  Regression
Descriptive

Describes
characteristics of
data from a sample.
Ex: Mean, standard
deviation, frequency,
and percentage.
Descriptive Statistics

Ex: The number of
positive malaria
cases among a sample
of 150 pregnant
women was 60
(40%).
Can we estimate
Prevalence of malaria
among the
population?
Inferential Statistics

Why medicine and
health science
students need to
learn Biostatistics?
To be able to:
 Conduct research
 Identify health
problems
 Monitor and evaluate
health programs

Variables
• Variable is any characteristic that
differs between individuals, Time or
place.
• Example of Variables:
• (1) No. of patients (2) Height
• (3) Sex (4) Educational Level

Types of Statistical variables
1.Quantitative/Numerical Variable: is a
characteristic that can be measured in
numbers.
Examples:
(i) Family Size (ii) No. of patients
(iii) Weight (iv) height (v) Age

Types of Quantitative Variable
a) Discrete Variables: quantitative variables
with no decimals or have gabs b/w numbers
Examples: Family size, Number of patients, No. of
students, parity, gravidity
(b) Continuous Variables: Quantitative variables
with decimals or have no gabs b/w numbers
Examples: Height, weight, income, blood sugar
level, creatinine level.

2. Qualitative/Categorical Variable: is a
characteristic that its values can be divided
into categories. No numbers!
Example:-
Blood type, Nationality, Students Grades,
Educational level.

Discrete
(whole
number)
Qualitative Quantitative
Continuos
(Decimal)
Variable
Type:
Nature:

Statistical scales of Measurements
• Nominal Scale: implies name only no order or rank is
involved. E.g. Sex, blood type, institutional departments,
nationality.
• Ordinal Scale: implies name and order or rank. E.g.
Educational Level, military rank, students’ grades.
• Interval Scale:0 does implies presence of the
characteristics. E.g. Temperature and pH
• Ratio: 0 imply absence of the characteristics
both Interval and Ratio between two numbers – are
meaningful. eg. Height, Weight, age, income

Qualitative Quantitative
Variable
Interval
Ratio
Type:
Scale: Ordinal
Nominal

Variables Types Scales of
measurement

Population (parameter) vs Sample (statistic)
population is the largest collection of
objects (elements or individuals) in which
we want to draw some conclusions.
Populations may be finite or infinite.

Population Size (N): The number of
elements in the population and is denoted by
(N). Ex: 100 students in a class.
Sample: - A sample is a part of a population
from which we collect the data and is
denoted by (n). Ex: 20 students out of 100
students in a class.

Population Sample
Statistic
Parameter

Common statistical symbols
Title Symbol
Sample Mean x
Population mean 
Sample standard deviation s
Population standard deviation 
Sample variance s2
Population variance 2
Summation 
Correlation coefficient r
Coefficient of determination r2
Degree of freedom df

Title Symbol
Chi-square value 2
Sample proportion p
Population proportion ∏
Null hypothesis Ho
Alternative hypothesis H1 or HA
Sample Size n
Type I error  error
Type II error  error
Power of the test 1- 

BIOSTATISTICS LESSON 1 INTRODUCTION-1.pdf

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