This document provides an introduction to biostatistics, including definitions, types of data, scales of measurement, and methods of data collection. It defines biostatistics as statistics applied to biological and health problems. Descriptive statistics are used to collect, organize, summarize and present data, while inferential statistics generalize from samples to populations using probabilities. Data can be qualitative, involving categories without numerical values, or quantitative, having numerical values. Methods of collecting data include observation, interviews, questionnaires, and reviewing documentation. The appropriate method depends on the type of data needed and available resources.

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Introduction to
Biostatistics
Introduction to
Biostatistics
Lecture by:
Gurmesa Tura (MPH)
March 2011
AAU

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Objectives
At the end of this lecture the students will
be able to:
Define statistics & Biostatistics
Explain the roles of statistics in medicine
Describe the types of data and scales of
measurement
 Identify different methods of data collection

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Contents
Definition
Types of statistics
Roles of statistics
Types of data & scales of measurement
Data collection methods

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What is statistics?
The scientific study of numerical
data based on variation in nature.
(Sokal and Rohlf)
A set of procedures and rules for
reducing large masses of data into
manageable proportions allowing
us to draw conclusions from those
data. (McCarthy)

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Statistics…
Statistics is the art and science of
making decisions in the face of
uncertainty
Statistics is the science of collecting,
summarizing, presenting, interpreting
data, and of using them to test
hypotheses.
Biostatistics is statistics applied to
biological and health problems

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What are statistical data?
Observation: information obtained from
a single person
Data: information gathered from group
of people
Statistical data: raw materials or facts
of any statistical observations arising
when ever measurements are made or
observations are classified

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Types of statistics
Descriptive Statistics
– Collection,
– organization,
– summarization, and
– presentation of data.
Inferential Statistics
– Generalizing from samples to populations
using probabilities.
– Performing hypothesis testing,
– Determining relationships between variables,
– Making predictions.

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Why study statistics in medicine?
Medicine and epidemiology are
becoming increasingly quantitative
Knowledge of statistics is required
to design, conduct and analyse
medical researches
Helps for better understanding of
medical literature

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Roles of statistics
In clinical medicine
– Making clinical diagnosis
– Determining Rx and prognosis
– Handling variations (defining normal values
and normal ranges)
In public health
– Community diagnosis
In Research
– Designing and undertaking clinical & public
health research

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Uses of statistics
1. Collecting data in the best possible
way
2. Describing a characteristics of a group
or population
3. Analyzing and interpreting data
4. Making generalization about
populations based on studies of
samples

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Limitations of statistics
1. Statistics doesn’t deal with single (individual)
value.
– It deals only with aggregate values
1. Statistics can’t deal with qualitative
characteristics
– Deals with data which can be quantified
1. Statistical conclusions are not universally
true
– Context specific
1. Statistical interpretations require high degree
of skill & understanding of the subject.

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Types of data
 Based on source :
– Primary & secondary data
1. Primary data
• Data collected by the investigator for
the purpose of specific study
• Original in character
• Mostly generated by surveys
• Complete, reliable and more accurate

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Types of data…
2. Secondary data
When the investigator uses data which have
been collected by others for other purpose
Obtained from Journals, reports, Gov’t
publications etc
Less expensive (less money & time)
May be incomplete, less quality, less valid

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Scales of measurement
 Variable is any aspect of an
individual or thing that is
measured and can take any value
for different individuals or cases
 Divided in to two
1. Qualitative (categorical) variable &
2. Quantitative (numerical) variables

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Qualitative (categorical) variable
A variable which can not be
measured in quantitative
(numerical) form but can only be
identified by names.
It has two forms based on scales of
measurement
– Nominal
– Ordinal

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Nominal scale
Represent categories or names
There is no orders in the categories
It has two forms:
– Dichotomous- has 2 value categories
• E.g. Sex: Male or Female
• Immunization: yes or No
• Diseases outcome : Died or survived
– Multichotomous: >two categories
• E.g.
– Blood group: A, B, AB or O
– Marital status: single, married, divorced or
widowed

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Ordinal scale
Have order in the response categories
But, the distance or interval between categories
are not necessarily equal
– E.g Immunization status:
• Not immunized,
• Partially immunized
• Fully immunized
• Disease state
• Mild
• Moderate
• Severe
• Agreement questions
• Strongly agree
• Agree
• Indifferent
• Disagree
• Strongly disagree

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Quantitative (numerical) variables
Variables which assume numerical values.
 variables to which a number is assigned as a
quantitative value
Has two forms
– Discrete Variables
• Variables which assume a finite or countable number
of possible values.
• Usually obtained by counting. No decimal
Eg. - House hold size
- No. children
– Continuous Variables
• Variables which assume an infinite number of possible
values.
• Usually obtained by measurement.
• Can have decimals
• Eg. Age, weight, height

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Quantitative …..
 Continues variables…
 Has two scales of measures
 Interval scale:
– Order and distance implied. Differences can be compared;
– no true zero.
– Ratios can not be compared.
E.g. Temperature in Celsius.
 0O
c is not to mean there is no temperature
 40O
c is not twice as hot as 20O
c
 Ratio scale:
– Order and distance implied.
– Differences can be compared;
– has a true zero.
– Ratios can be compared.
– Examples: Height, weight, blood pressure
• 40cms is twice as long as 20cms
• 0 cm is true 0 as there is no 0zero height

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Discrete

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Data collection
The process of obtaining statistical data
Before any statistical work can be done
data must be collected
Collecting Primary data
– Observation
– Interview
– Use of self administered questionnaire
Collecting secondary data
– Use of documentary sources

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Observation
Systematically selecting , watching and
recording behaviours of people or other
phenomena and aspects of the settings in
which they occur
For the purpose of obtaining specified
observation
Includes
– Visual observation
– Radiographic, Biomedical, x-ray,
microscope, clinical examinations, etc

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Observation…
It can also be used In observing
behaviour of people, culture etc.
It could be
– Participant observation or
– Non-participant observation

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Observation…
Advantage
– More accurate data on behaviour or
activity
Disadvantages
– Observer bias
– Prejudice
– Desirability bias
– Needs skilled human power in high
level machines

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Interviews
Face to face interview
Telephone interview
Group interview or Focused Group Discussion
(FGD)
Self administered questionnaire
Mailed questionnaire
Computer interview

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Face to face interview
Advantage
– Permits detailed & in-depth questions &
responses
– Minimizes non-response
Disadvantage
– Costly
– Interviewer bias
– Investigator bias
– Interviewer cheating

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Telephone interview
Advantage
– Convenient
– Saves time
– Relatively inexpensive
– Less interviewer & investigator bias than
personal interview
Disadvantage
– Non-coverage
– Limited length & depth of questions and
responses

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Self-administered Questionnaire
Advantage
– Cost effective for large areas
– Minimizes interviewer bias
– Promotes accurate answers
– Sensitive issues can be gathered
Disadvantage
– Low response rates
– Unanswered questions
– Incorrect answers

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Mailed questionnaire
Advantage
– Allows collecting data with out
personal presence
Disadvantage
– Low response rate
– Not applicable for illiterates
– Low coverage in rural areas

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Use of documentary sources
These include
– Clinical & other personal records
– Vital statistics
– Census data
Sources
– Official publications of CSA
– Publications of MOH & other ministries
– News papers & journals
– International publications (WHO, UNICEF,
etc)
– Health facilities’ records

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Choosing method of data collection
Choosing which method(s) of data
collection depends on:
– Type of data we need
– Resources (time, personnel & facility)
– Accuracy & strength of the method
– Acceptability of the method by the
subjects
– Back ground of study subjects
– Etc

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Thank you!