This document provides an introduction to biostatistics. It discusses how biostatistics is the application of statistical methods to biological and medical sciences. Biostatistics is important for organizing health information, assessing health status, evaluating health programs and resources, and determining the strength of associations between exposures and health outcomes. It also covers key aspects of biostatistics including research design, data analysis and interpretation. The document emphasizes that biostatistics allows drawing inferences about populations based on samples.

1. CHAPTER ONE
INTRODUCTION TO STATISTICS

2. 2
Introduction
• What is statistics?
• Statistics: A field of study concerned with:
– collection, organization, analysis, summarization
and interpretation of numerical data, &
– the drawing of inferences about a body of data
when only a small part of the data is observed.
• Statistics helps us use numbers to
communicate ideas

3.  Biostatistics: The application of statistical
methods to the fields of biological and
medical sciences.
 Concerned with interpretation of biological
data & the communication of information
derived from these data
 Has central role in medical investigations
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Importance of biostatistics in health
sciences
• Provide methods of organizing information
• Assessment of health status
• Health program evaluation
• Resource allocation
• Evaluation of magnitude of association
– Strong vs. weak association between exposure
and outcome

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• Assessing risk factors
– Cause & effect relationship
• Evaluation of a new vaccine or drug
– What can be concluded if the proportion of
people free from the disease is greater among the
vaccinated than the unvaccinated?
– How effective is the vaccine (drug)?
– Is the effect due to chance or some bias?
• Drawing of inferences
– Information from sample to population
Importance of biostatistics in health
science

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What does biostatistics cover?
Research Planning
Design
Execution (Data collection)
Data Processing
Data Analysis
Presentation
Interpretation
Publication
Biostatistical
thinking
contribute in
every step in a
research
The best way to
learn about
biostatistics is to
follow the flow of a
research from
inception to the
final publication

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Research Design
• We can not study all subjects (all pregnant
women, or all people) living in a given
geographical area
– Sampling technique
– Inclusion/exclusion criteria
– Sample size calculation
– Study design
– Method of data collection
– Etc

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Analysis
• Analysis part is the major part of learning
about biostatistics
– There are dozens of different methods of
analysis, which makes difficult the choice of the
correct method for a particular case
– It is necessary to consider the philosophy that
underlies all methods of analysis:
• Use data from a sample to draw inference about a
wider population

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Interpretation
• Interpretation of results of statistical
analysis is not always straightforward, but is
simpler when the study has a clearer aim
• If the study has been well designed and
correctly analyzed the interpretation of
results can be fairly simple

10. Types of Statistics
1. Descriptive statistics:
• Ways of organizing and summarizing data
• Helps to identify the general features and
trends in a set of data and extracting useful
information
• Example: tables, graphs, numerical summary
measures
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11. Types of Statistics
2. Inferential statistics:
• Methods used for drawing conclusions about
a population based on the information
obtained from a sample of observations
drawn from that population
• Example: point estimation, interval estimation
( e.g. confidence interval), comparison of two
or more means or proportions, hypothesis
testing, etc.
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Data
• Data are numbers which can be measurements or
can be obtained by counting
• The raw material for statistics
• Can be obtained from:
– Routinely kept records, literature
– Surveys
– Counting
– Experiments
– Reports
– Observation
– Etc

13. The statistical data may be classified under two
categories, depending upon the sources.
1. Primary data: collected from the items or individual
respondents directly by the researcher for the
purpose of a study.
2. Secondary data: which had been collected by
certain people or organization, & statistically
treated and the information contained in it is used
for other purpose by other people
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Population and Sample
• Population:
– Refers to any collection of objects
• Target population:
– A collection of items that have something in common
for which we wish to draw conclusions at a particular
time.
• E.g., All hospitals in Ethiopia
– The whole group of interest

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Population and Sample
Study Population:
• The subset of the target population that has at least
some chance of being sampled
• The specific population group from which samples
are drawn and data are collected

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Population and Sample
Sample:
. A subset of a study population, about which
information is actually obtained.
. The individuals who are actually measured and
comprise the actual data.

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Population
Sample
Information
• Role of statistics
in using information
from a sample to make
inferences about the
population

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Sample
Study Population
Target Population
E.g.: In a study of the prevalence
of HIV among adolescents in
Ethiopia, a random sample of
adolescents in Lideta Kifle
Ketema of AA were included.
Target Population: All
adolescents in Ethiopia
Study population: All
adolescents in Addis Ababa
Sample: Adolescents in Lideta
Kifle Ketema who were included
in the study

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Generalizability
• Is a two-stage procedure:
• We need to be able to generalize from:
– the sample to the study population, &
– then from the study population to the target
population
• If the sample is not representative of the
population, the conclusions are restricted to
the sample & don’t have general applicability

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Collect information
from a relatively
SMALL sample
Draw conclusions
about a rather
LARGE population

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Parameter and Statistic
• Parameter: A descriptive measure computed
from the data of a population.
– E.g., the mean (µ) age of the target population
• Statistic: A descriptive measure computed
from the data of a sample.
– E.g., sample mean age ( )