This document provides objectives and details about a biostatistics course. The general objective is to present numerical data in a way that allows for valid interpretations. Specific objectives include designing, organizing, and summarizing data, as well as understanding probabilities, statistical analyses, and how to make inferences about populations based on sample data. The course will cover topics like data collection and presentation, probability distributions, statistical inference through estimation and hypothesis testing, and determining sample sizes. Students will be evaluated based on exercises and a final exam. Computer software will be used to analyze data.

1. School of Public Health
Biostatistics

2. Objectives
General Objective
♦ The aim of biostatistical study is to provide the numbers that
contain information about certain situations and present them in
such a way that valid interpretations are possible
Specific Objectives
♦ design, organize, present and summarize data
♦ understand the process involved in data collection and
processing
♦ distinguish between categorical and numeric data
♦ understand probabilities and their applications
♦ interpret summary statistics, graphical displays and contingency
tables commonly presented in the health literature
♦ carry out exploratory data analysis
♦ understand the process involved in estimations and hypothesis
testing
♦ interpret the functions of confidence intervals and p-values
♦ give an interpretation or reach a conclusion about a population
on the basis of information contained in a sample drown from
that population.

3. Course contents
1.Introduction to Biostatistics
2.Data and types of data
3.Scales of Measurement
4.Presenting and summarizing data
5.Measures of central tendency and measures of dispersion
6. Probability and probability distributions
► Sampling and Sampling distributions
7. Statistical inferences
► Point and interval estimation
► Hypothesis testing
► Test of significance
► Concept of P-value
► Interpreting and communicating results
► sample size determination
8. Using available information to measure health and disease
(health information system)

4. Use of Computers
♦ The use of statistical computer package for processing and analysis
of data is an integral component of this course.
♦ What the microscope is to biology and what the telescope is to
astronomy, the computer is to epidemiological research.
♦ Computers are reliable, portable, computationally powerful and
easy to use. However they give you what you give them. Computer
scientists refer to this principle as GIGO Garbage In, Garbage Out.
♦ There are many data base computer programs that can be used for
data entry purposes.
♦ It is however helpful, if the data is entered into a program which is
compatible with the statistical package to be used for analysis.
♦ In this course, we will use the Epi-Info, SPSS statistical package.

5. References: (available in the Library of
the Department of Community Health)
1. Gordis, L. (2009). Epidemiology (4th ed.). USA. Elsevier Inc.
2. Koepsell & Weiss. Epidemiologic Methods. Oxford University Press,
2003.
3. Last (ed.) Dictionary of Epidemiology, 1995
4. Rothman, Kenneth J.; Greenl and, Sander; Lash, Timothy L. Modern
Epidemiology, 3rd Ed Lippincott Williams & Wilkins.2008.
5. Martin Bland. An introduction to Medical Statistics
6. Colton T. Statistics in Medicine
7. Daniel W. Biostatistics a foundation for analysis in the Health Sciences
8. Kirkwood BR. Essentials of Medical Statistics
9. Knapp RG, Miller MC. Clinical epidemiology and Biostatistics. Baltimore
Williams and Wilkins, 1992
10. Pagano & Gauvereau. Principles of Biostatistics
11. Schelesslman, J.J. Case control studies, Design, Conduct, Analysis, Oxford
University Press, New York, 1982
12. Breslow, N.E. Statistical Methods in cancer Research, Volume I-The analysis
of case-control studies

6. Teaching Methods & Evaluation
♦ Instructors:
– Awgichew Kifle (Lecturer of Biostatistics)
♦ Teaching Methods
– Lecture
– Exercise
– Group presentations
– Computer
♦ Evaluation
– Exercise: 20%
– Final Exam: 30%

7. Introduction to
Biostatistics

8. Objectives
– Define Statistics
– Explain Descriptive statistics
– Explain Inferential Statistics
– Define Biostatistics
– Able to explain the rational of studying
biostatistics
– Understand the use of biostatistics

9. Consider the following data
♦ Polio Vaccine: In the 1950’s polio was a
serious disease that affected countless people
(mostly children)
♦ In 1954,
401,974 children vaccinated
201,229 with a trial vaccine and
200,745 with a placebo
There were a total of 143 Polio cases:
110 for placebo
versus
33 for vaccine

10. Cont.….
There were a total of 143 Polio cases:
110 for placebo versus 33 for vaccine
♦ What does this mean?
♦ How did you get the 400 000 children?
♦ How did you divide them into two groups?
♦ Suppose you could repeat the experiment, how
likely you would make the same conclusion?
♦ How much confidence do you have to detect the
difference between the two groups?

11. 1. Introduction to Biostatistics
♦ Introduction
Statistics: 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.
Branches of Statistics
1. Descriptive statistics:
2. Inferential statistics:
Descriptive statistics: deals with the description of data in a clear
and informative manner using tables and graphs.
►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
► uses tables, graphs, numerical summary measures

12. Descriptive Statistics
♦ Collect data
– e.g., Survey
♦ Present data
– e.g., Tables and graphs
♦ Summarize data
– e.g., Sample mean =
i
X
n


13. Cont.…
Example: the number of students in a class is 300.
the number of patients in a hospital is 260.
2. Inferential statistics: deals with techniques of making conclusions
about the population based on the information obtained from a
sample drawn from that population.
The inferences are drawn from particular properties of sample to
particular properties of population.
Inferential statistics builds upon descriptive statistics.
Example: Principles of probability, estimation,, hypothesis testing, etc.

14. Example: Adane concludes that his chance of passing the first
year this academic year is at least 80% based on the statistics
that 75% of the freshmen passed last year.

15. Inferential Statistics
♦ Estimation
– e.g., Estimate the population
mean weight using the sample
mean weight
♦ Hypothesis testing
– e.g., Test the claim that the
population mean weight is 120
pounds
Inference is the process of drawing conclusions or making
decisions about a population based on sample results

17. Biostatistics: An application of statistical method to biomedical
science.
► Has central role in medical investigations
► Concerned with interpretation of biological data & the
communication of information about data
Rationale of studying biostatistics
♦ Statistics pervades a way of organizing information on a wider
and more formal basis than relying on the exchange of story or
biography and personal experience.
♦ The medical and public health literatures are sated or full with
reports in which statistical techniques are used extensively.
♦ The planning, conduct, and interpretation of much of medical
and public health research are becoming increasingly reliant on
statistical technology.

18. Uses of Statistics in Public Health and Medicine
 Provide a way of organizing information
 Assessment of health status
 Health program evaluation
 Assessing risk factors
 To formulate scientific questions to be answered, collect,
analyze and interpret data
 Cause & effect relationship
E.g. Evaluation of a new vaccine or drug
How effective is the vaccine (drug)?
Is the effect due to chance or some bias?

19. Uses of biostatistics
 Drawing of inferences
 Information from sample to population
 Magnitude of association
 Strong vs weak association between exposure and outcome

20. Characteristics of statistical data
 They must be in aggregates: A single fact, even though
numerically stated, cannot be called statistics
 They must be affected by a multiplicity of causes
 They must be reasonably accurate.
 They must have been collected in a systematic manner for a
predetermined purpose.
 They must be comparable either in point of time, space or
condition.

21. Characteristics of statistical data
 They must be in aggregates: A single fact, even though
numerically stated, cannot be called statistics
 They must be affected by a multiplicity of causes
 They must be reasonably accurate.
 They must have been collected in a systematic manner for a
predetermined purpose.
 They must be comparable either in point of time, space or
condition.

22. Stages in statistical investigation
There are five stages or steps in any statistical investigation.
Data collection: is the process of measuring, gathering and
assembling the raw data up on which the statistical investigation
is to be used.
Organization of data: it can be defined as the summarization
of data in some meaningful ways. For example, table form.
Presentation of data: the process of re-organization,
classification, compilation, and summarization of data to present it
in a meaningful form.

23. Stages in statistical investigation
Analysis of data: the process of extracting relevant information
from the summarized data, mainly through the use of elementary
mathematical operation.
Inference of data: the interpretation and further observation of
the various statistical measures through the analysis of the data
by implementing those methods by which conclusions are formed
and inferences made.

24. Definition of Some Basic terms
Population: is the totality of all subjects possessing certain
common characteristics that are being studied.
It is a collection of all units under investigation, which consists of a
specified type of person or objects over a given space and time.
Population should be defined on the basis of the objective of the
study by the investigator.
For example, it could mean the total human population of a
country or village or a state; total number of school age population;
total number of women in the reproductive age group, etc.
Sample: is a sub group or part of the population selected by some
methods in order to estimate the characteristics of the population.

25. Cont.…
Parameter: Characteristic or measure obtained from a
population.
Statistic: Characteristic or measure obtained from a sample.
Sampling: The process or method of sample selection from the
population.
Sample size: The number of elements or observation to be
included in the sample.

26. Census
♦ Refers to a nation-wide periodic counting
(enumeration) of the population.
♦ There are two main different schemes for
enumerating a population in a census.
1. De jure: - the enumeration (or count) is
done according to the usual or legal place
of residence.
♦ A de jure count of the members of a
household excludes temporary residents
and visitors, but includes permanent
residents who are temporarily away.

27. Census…
a) Advantage
♦ information unaffected by seasonal
and other temporary movements of
people.
b) Disadvantages
♦ Some persons may be omitted from
the count while some others may be
counted twice.

28. census…
♦ It is difficult to be sure just which is a
person’s usual or legal residence. (in
places where mobility is high and no
fixed residence is indicated).
♦ Information collected regarding persons
away from home is often incomplete or
incorrect.

29. census…
2. De facto: - the enumeration is done according
to the actual place of residence on the day of the
census.
♦ A de facto count includes temporary residents
and visitors, but excludes permanent
residents who happen to be away on the day
of the census.

30. census…
a) Advantages
– Gives less chance for the omission of persons
from the count
b) Disadvantages
♦ Population figures may be inflated or deflated by
tourists, travelling salesmen, and other transients.
♦ In areas with high migration, the registration of
vital events is subject to distortion.

31. 3
Surveys
♦ A survey is a technique based on sampling methods
by means of which we try to obtain specific
information from part of the population considered
to be representative of the whole.
♦ Surveys are made at a given moment, in a specific
area; sporadically and without periodicity for the
deep study of the problem.

32. Limitations of statistics
 It deals with only those subjects of inquiry that are capable
of being quantitatively measured and numerically expressed.
 It deals with aggregates of facts and no importance is
attached to individual items
 Statistical data are only approximately and not
mathematically correct.

33. Exercise
Classify the following sentences as belonging to the area of
descriptive statistics or inferential statistics.
♦ The average systolic blood pressure record of 15 women in a
village was 140.
♦ Of all patients who received this particular type of drug at a
clinic Y, 75% later developed significant side effect.
♦ For next three month it is predicted that there will be short
hospital stay of patients.
♦ Among students which have been tested HIV AIDS last year
in Addis Ababa University 0.05% of them are positive.

34. THANK YOU!!!