2. At the end of the course students will able to;
– The role of biostatistics in health sciences and the use of
biostatistical methods in field of health sciences
– Describe methods of data collection, recording, coding and
handling data
– Compute numerical summary measures and present data
– Identify and make use of data from existing health record
Course objectives:
2
3. – Apply different techniques of sampling and determine
sample size
– Explain the context and meaning of statistical estimation
and statistical significance
– Measure associations between outcome and exposure
Course objectives, …
3
4. 1. Introduction
2. Measures of central tendency and dispersion
3. Probability and probability distribution
4. Sampling and sampling distribution
5. Basic Concepts of Inference
6. Introduction to correlation and Regressions
Course contents
4
5. 1. M. Pagano & K. Gauvereau: Principles of Biostatistics
2. Daniel W. : Biostatistics:AFoundation for analysis in Health
Sciences
3. Colton T. : Statistics in Medicine
4. Bland M. :An Introduction to Medical Statistics
5. David S. Moor, G.P.McCable: Introduction to the practice of
Statistics
6. Kleinbaum, K.Muller:Applied Regression
7. Analysis and other Multivariate Methods
8. L. D. Fisher & G. Van Belle: Biostatistics
References
5
6. 8. Kirkwood B. : Essentials of Medical Statistics
9. A. R. Feinstein: Principles of Medical Statistics
10. R. G. Knapp & M. C. Miler: Clinical epidemiology
and biostatistics
11. D. J. Sheskin: Hand book of Parametric and
Nonparametric Statistical Procedure
12. Armitage P. & Berry G. : Statistical Methods in
Medical Research
13. P. S.R.S. Rao: Sampling methodologies with
application
14. R.N.Forthofer & E. S. Lee: Introduction to
Biostatistics
References, …
6
8. • Why we need Biostatistics in Public Health?
• What is public health?
– “Public Health is the science and art of preventing disease,
prolonging life and promoting health through the organized
efforts of society.” (WHO)
Chapter 1
Introduction
8
9. • Assessment: Identify problems related to the public’s health,
and measure their extent
• Policy Setting: Prioritize problems, find possible solutions, set
regulations to achieve change, and predict effect on the
population
• Assurance: Provide services as determined by policy, and
monitor compliance
• Evaluation: how well are they performed?
The Functions of Public Health
9
10. • A field of study concerned with the collection, organization and
summarization of data, and the drawing of inferences about a
body of data when only part of the data are observed.
• The science of collecting, describing, and interpreting data
• Help scientists to describe our perception of the world
What is statistics
10
11. What is biostatistics
11
• Deals with the collection, organization, summarization, and
analysis of data in the fields of biological, health, and medical
sciences including other life sciences
• An application of statistical method applied to life and health
sciences
• Statistical tools/methods applied to biological phenomenon
characterized by the resultant of interaction between the
genetic architecture and the environmental factors under which
lives exist
12. What is biostatistics,…
12
• Emerged as one of the most important disciplines in recent
decades due to;
– interactive advancements in the fields of statistics,
computer science, and life sciences
• To address new challenges due to new sources of data and
growing demand for biostatisticians with sound background to
face the needs
13. What is biostatistics,…
13
• Biostatistics deals with designing studies, analyzing data, and
developing new statistical techniques to address the problems
in the fields of life sciences
• It includes statistical analysis with special focus to the needs in
the broad field of life sciences including public health,
biomedical science, medicine, biological science, community
medicine
14. What is biostatistics,…
14
• At the current stage of information boom in every sector, we
need to attain an optimum decision utilizing the available data.
• The information used for making decision through a statistical
process is called data.
• The decision about the underlying problem is to be made on
the basis of a relatively small set of data that can be
generalized for the whole population of interest
15. • Experimenter faces two challenges:
1. to find the values that summarize the basic facts about the
unknown characteristics of the population as sought in the
study
2. to make sure that the values obtained have adequate
statistical support for generalizing the findings for the
domain or more specifically the population from where the
sample is drawn
15
16. • The fundamental objective is to learn the basics about two
major aspects of statistics:
I. descriptive statistics
II. inferential statistics.
16
17. Major aspect of statistics
17
• The descriptive statistics deals with organization,
summarization, and description of data using simple statistical
techniques
• the inferential statistics link the descriptive statistics measures
from sample with the larger body of data, called population
from which the smaller data sets are drawn
18. Population & Sample
18
• The information used to decision about the underlying
problem is to be made on the basis of a relatively small set of
data that can be generalized for the whole population of
interest
• The term population is used with specific meaning and a
defined domain
19. Population
19
• E.g. 1
In a study of disease among under five children;
• The population is comprised of every child of under 5
years of age
In the study substance use among university students
The study population is students studying in universities
20. Population (N)
20
• Target population:Acollection of items that have something
in common for which we wish to draw conclusions at a
particular time
• E.g.
– Under five children in Harar
– Haramaya University students
– Adults 25 years or older in Dire Dawa
21. Population, …
21
• Study Population: The specific population from which data
are collected or accessible population
• E.g.
– under five children in Harar in Hakim Woreda
– Haramaya University students in CHMS
– Adults 25 years or older in Kebele 8
22. Sample (n)
22
• Asubset of a study population, about which information is
actually obtained
• In reality, it is difficult to conduct the study on the whole
population due to;
– cost
– Time
– skilled manpower needed to collect quality data
23. Sample,…
23
• E.g.
– under five children in randomly selected from Hakim
Woreda
– Haramaya University students randomly selected from
Schools and department in CHMS
– Adults 25 years or older in randomly selected from Kebele
8 in Dire Dawa
25. Parameters and statistics
25
• Parameter: Adescriptive measure computed from the data of
a population
– E.g
• polation mean (µ)
• Population variance(σ2)
• Population standard deviation (σ), etc
Statistic:Adescriptive measure computed from the data of a
sample. E.g.
• Sample mean(𝑥)
• Sample variance (S2)
• Sample standard Deviation (S), ete.
26. Data
26
• Raw material of statistics
• numbers that resulting from the taking a measurement and
those that result from the process of counting.
27. Variable
27
• Characteristic that it takes on different values in different
persons, places, or things
• Some examples of variables include diastolic blood pressure,
heart rate, the heights of adult males, the weights of preschool
children, and the ages of patients seen in a dental clinic
28. Types of variable
28
• Quantitative Variables
– that can be measured in the usual sense
– Measurements made on quantitative variables convey
information regarding amount
– Example; obtain measurements on the heights of adult
males, the weights of preschool children, and the ages of
patients seen in a dental clinic
29. Quantitative variable
29
Divided into two;
1. Discrete
• Characterized by presence of gaps between to
subsequent number
• E.g. number of patients admitted, number empty bed
2. Continuous
• Characterized by absence of gaps between two
consecutive number
• E.g. weight, height, bloods pressure, BMI, etc.
30. Qualitative Variables
30
• Characteristics are not capable of being measured
• Many characteristics can only be categorized only
• Measurements made on qualitative variables convey
information regarding attribute.
• Example: when an ill person is given a medical diagnosis, a
person is designated as belonging to an ethnic group, or a
person, place, or object is said to possess or not to possess
some characteristic of interest
31. Qualitative variable,…
31
• Divided into two
1. Nominal variable
– Name or symbol assigned to characteristic being measured
– Categories in variable are mutually exclusive
– E.g. sex, cause of disease, caused of death, marital status,
place of residence
32. 2. Ordinal variable
– Similar to nominal attribute assigned to characteristics
– Categories are not only mutually exclusive, but also have
some order or rank
– E.g. cancer stage, class performance, height as
taller/average/shorter, condition of patients;
unimproved/slightly improved, improved, much improved,
etc.
32
33. Scale of Measurement
33
• Clearly not all measurements are the same.
• Measuring an individuals weight is qualitatively different from
measuring their response to some treatment on a three category
of scale, “improved”, “stable”, “not improved”.
• Measuring scales are different according to the degree of
precision involved.
• There are four types of scales of measurement.
34. Scale of measurement,…
34
1. Nominal scale:
– uses names, labels, or symbols to assign each measurement
to one of a limited number of categories that cannot be
ordered.
– Used only to qualitatively classify or categorize not to
quantify
• Examples: sex, race, marital status, caused diseases,
Blood type, causes death, religions
35. Scale of measurement,…
35
2. Ordinal scale:
– characterized by the ability to measure a variable in terms
of both identity and magnitude
– Categorize a variable and its relative magnitude in relation
to other variables
– assigns each measurement to one of a limited number of
categories that are ranked in terms of a graded order.
• Examples: Patient status, Cancer stages
36. Scale of measurement,…
36
3. Interval scale:
– Builds on ordinal measurement by providing information about both
order and distance between values of variables
– assigns each measurement to one of an unlimited number
of categories that are equally spaced. It has no true zero
point.
– The numbers on an interval scale are scaled at equal
distances.
37. Scale of measurement,…
37
Interval scale,…
– Numbers scaled at equal distances
– No absolute zero point; zero point is arbitrary.
• Example:
– Temperature measured on Celsius or Fahrenheit
– Time of each day in the meaning of a 12-hour clock
– IQ test (intelligence scale)
– Measuring an income as a range
38. Scale of measurement,…
38
4. Ratio scale:
– measurement begins at a true zero point and the scale has
equal space.
– Allow for the use of sophisticated statistical techniques.
– Examples: Height, weight, Age, budget, number of
students, area
39. The end !
THANK YOU
FOR
THANK YOU
FOR
!!
THANK YOU FOR YOURATTENTION!!
39
