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.
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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.
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. 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
4. 4
• The numbers must be presented in such a
way that valid interpretations are possible
• Statistics are everywhere – just look at any
newspaper or the current medical and
public health literature.
5. 5
Uses of biostatistics
• Provide methods of organizing information
• Assessment of health status
• Health program evaluation
• Resource allocation
• Magnitude of association
– Strong vs weak association between
exposure and outcome
6. 6
Uses of biostatistics
• 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
7. 7
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
8. 8
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
9. 9
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
10. 10
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
11. 11
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
• Also very important in conveying the final
results of a study
• Example: tables, graphs, numerical
summary measures
12. 12
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: Principles of probability,
estimation, confidence interval,
comparison of two or more means or
proportions, hypothesis testing, etc.
13. 13
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
14. 14
Types of Data
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
15. 15
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
16. 16
Population and Sample
Study (Sampled) 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
17. 17
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.
19. 19
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
20. 20
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
22. 22
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 ( )
Editor's Notes
#5: Assessment of health status: Vital statistics
Resource allocation: Census
