introduction to probability

1. CHAPTER ONE
INTRODUCTION TO STATISTICS

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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 ( )