The document provides an overview of biostatistics and its key concepts. It discusses the objectives of biostatistical study as presenting data in a way that allows for valid interpretations. It outlines topics covered in the course including different data types, scales of measurement, data collection techniques, and the use of computers for statistical analysis. References for further learning are also provided.

1. Basics of Biostatistics
December 2022

2. 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. outlines
• Introduction to the course
• Data and types of data
• Scales of measurement
• Data collection techniques

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
• Thomas J. Quirk • Simone Cummings. Excel 2010 For Health Services Management Statistics A
Guide To Solving Practical Problems. 2014
• Richard Shiavi , Introduction To Applied Statistical Signal Analysis: Guide To Biomedical And
Electrical Engineering, Applications. 2007
• Sun-Chong Wan G Arturas Petronis, Dna Methylation Microarrays Experimental Design And
Statistical Analysis. 2008
• David Stockwell, Niche Modeling Predictions From Statistical Distributions. 2007.
• Daniel W. Biostatistics: A Foundation for analysis in Health Sciences. 2006
• Martin bland : An introduction to medical statistics. Oxford University press . London. 3rd edition.
2009.
• Alan Agresti : An introduction to categorical data analysis. Wiley press:University of Florida . 2nd
edition. 2007.
• K R Sundaram, S N Dwivedi and V streenivas :Medical Statistics: Principles and methods: Anshan
limited publisher. UK. 2010
• Chap T. LE: Introduction to biostatistics. Wiley inter science publisher. Canada. 2003
• Norman and Streiner: Biostatistics: The Bare Essentials : Peoples medical publishing house publisher
. USA. 2008, 3rd edition
• Marcello Paganao and Kimberlee Gauvreau: Principles of biostatistics. Thomson learning Academic
resource center publisher, USA, 2000. 2nd edition

6. Teaching Methods & Evaluation
• Teaching Methods
• Lecture/ Discussion
• Group discussion and exercise within the class
• Continuous Evaluation
• Classroom exercise and presentation(optional)
• Home takes assignments

7. Descriptive Statistics

8. Objectives
• Define Statistics
• Explain Descriptive statistics
• Explain Inferential Statistics
• Define Biostatistics
• Understand the use of biostatistics

9. 1. Introduction to Biostatistics
• Statistics: A field of study dealing with the planning and design of data
collection, organization, presentation, analysis, interpretation and
drawing conclusion based on the data.
• In other words, statistics as a subject is a study of making sense of
data.
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

10. Descriptive Statistics
Includes the following
• Collect data
• e.g., Survey
• Present data
• e.g., Tables and graphs
• Summarize data
• e.g., Sample mean =
Example: The number of patients in a hospital is 150.
i
X
n


11. Inferential statistics
 deals with techniques of making conclusions about the population based on the information obtained from a
sample drawn from that population.
Inferences are drawn from particular properties of sample to particular properties of population.
Inferential statistics builds up on descriptive statistics.
 Principles of probability, estimation, hypothesis testing, etc.
Example:
1. The probability of having two car accident in Addis Ababa tomorrow is 0.6.
2. Adane concludes that his chance of passing the first year is at least 80% based on the statistics that 75% of
the freshmen passed last year.

12. 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

13. 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.
• 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 0.001% of them are positive.
• According to meteorologist report today, there is 70% chance
of having rain tomorrow in Addis Ababa.

14. Biostatistics
Biostatistics: An application of statistical method to biomedical science.
• Concerned with interpretation of biological data & the communication of
information about data.
• Has central role in medical investigations

15. Uses of Biostatistics in Health
• Provide a way of organizing information
• Hospital utility statistics
• Resource allocation
• Vaccination uptake
• Magnitudes of a disease/condition
Assess health status
Evaluate Health program
Assess risk factors
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?

16. Uses of biostatistics
Draw inferences
Information from sample to population
Making diagnosis and choosing an appropriate treatment
(implicit/probability)
Magnitude of association
Strong vs weak association between exposure and outcome

17. 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.
• 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.
Stages in statistical investigation

18. • But, before statistical investigation we should consider the plan and design of
the study.
• We can never study all diabetics, all pregnant women, or all people living in a
geographical area
–Method of data collection
–Sampling technique
–Inclusion/exclusion criteria
–Sample size
Research Design

19. • Target population: A collection of items that have something in common for which
we wish to draw conclusions at a particular time.
• Study Population: The specific population from which data are collected
• Objective of the study: to see the association of intimate partner violence during
pregnancy with preterm birth in Addis Ababa, Ethiopia
• Source population: All women who were formally married or have a partner, and
who attended delivery services in public hospitals of Addis Ababa were considered as
source population.
• Study population: Sampled women who fulfilled the inclusion criteria of source
population.
• Sample: A subset of a study population selected by some methods, about which
information is actually obtained in order to estimate the characteristics of the
population.
Define common terms

20. • 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.
Cont.

21. 1. Addis Ababa University decided to increase the tuition fee
starting from the next academic year. The student union wanted
to know what percentage of student support the fee and
randomly selected students from each department and found
that 35% of the student support the fee.
A) Identify the population and sample for this study?
B) Are 35% resulting from parameter or statistic? Explain.
Exercise

22. Cont.
• A Variable is a characteristic which takes different values in
different persons, places, or things. In general it is a
characteristic which takes different values.
• Variables are things that we measure, control, or
manipulate in research.
♦ Data: are measurements or observations (value) recorded
for each element. For example, data include record on
weight, length, breaking strength, age, sex, religion, marital
status, income etc.
Based on the nature of the variables we can have qualitative
and quantitative data.

23. Group Exercise
1.Define and explain the following terms
A) Biostatistics
B) Variables
C) Sampling
D) Sample size
E) Data
F) Variable
2. Name and define the two branches of statistics.
3. Mention and explain the uses of Biostatistics
4. List down and explain the stages in statistical investigation

24. 5. Explain the difference between the following statistical terms by giving example?
A) Parameter and statistic
B) Sample and Population
C) Qualitative and quantitative variable
D) Census and sample survey

25. Categorizing Data
• Can facilitate data analysis
• Must choose:
• Number of categories
• Category cut points
• Some options for cut points:
• Percentiles, natural breaks, established criteria
• Example: WHO body mass index classification
• Underweight: <18.50 kg/m2
• Normal: 18.50 – 24.99 kg/m2
• Overweight: ≥ 25.00 kg/m2

26. Cont.…
• Qualitative variable (categorical): implies attribute or quality
• We can Count the number of cases in each category.
• E.g., sex of a person, A study of treatment outcome of TB
• Quantitative variable: implies amount of quantity Or a variable that
can be measured numerically is called a quantitative variable.
E.g., height of a patient, weight of a child

27. Cont.… Discrete variables:
It can only have a finite number of values in any given interval. A variable whose
values are countable (expressed in whole numbers).
E.g. The number of episodes of diarrhoea a child has had in a year. You can’t have
12.5 episodes of diarrhoea
 The number of car accidents per a day in a given city
 the number of bacteria colonies on a plate,
• the number of cells within a prescribed area upon microscopic examination,
• the number of heart beats within a specified time interval,
• a mother’s history of number of births ( parity) and pregnancies (gravidity),
• the number of episodes of illness a patient experiences during some time period,
etc.

28. Cont.… Continuous variables
• It can have an infinite number of possible values in any given interval.
• A variable that can assume any numerical value over a certain interval or
intervals.
• The set of all values which consists of intervals, e.g. 0-9, 10-19, 20-29... etc.
• Example: Height, weight, age, blood pressure, serum cholesterol level,
income, and time are some of the examples.

29. 1. Year of birth
2. Marital status of women
3. Identification number of a study participant
4. Class rank
5. Length of infants at ANC clinic
Categorizing Variables-Exercise

30. Discrete or Continuous?
Identify whether the following data is discrete or continuous:
1. Distance from primary health center to reference lab
2. Number of times a child under 5 has experienced fever in the last month
3. Number of fatal accidents on a road over the past year
4. Weight gained or lost by a 9-month-old in the past 3 months

31. Categorizing Variables-Exercise
1. Year of birth: Quantitative
2. Marital status: Categorical
3. Identification number: Categorical
4. Class rank: Categorical
5. Length: Quantitative

32. Discrete or Continuous?
Identify whether the following data is discrete or continuous:
1. Distance from primary health center to reference lab: Continuous
2. Number of times a child under 5 has experienced fever in the last month:
Discrete
1. Number of fatal accidents on a road over the past year: Discrete
2. Weight gained or lost by a 9-month-old in the past 3 months: Continuous

33. Types of variables
Variables
Quantitative
Qualitative
Dichotomic Polynomic Discrete Continuous
Gender
hair color, marital
status
Children in family weight of a student

34. Types of data
Data:- The raw material of statistics is data and may
be defined as numbers.
• The two kinds of numbers that we use in statistics
are numbers that result from the taking of a
measurement, and from the process of counting.
• Primary data: collected from the items or
individual respondents directly for the purpose of
certain study.
• It needs the involvement of the researcher
himself. Example. Health Survey data

35. Con…
• Secondary data: which had been collected by
certain people or agency, and statistically treated
and the information contained in it is used for other
purpose.
• In this case data were obtained from already
collected sources like newspaper, magazines,
CSA, DHS, hospital records and existing data like;
• Mortality reports
• Morbidity reports
• Epidemic reports

36. Scale of measurement

37. Scales of Measurement
Clearly all measurements are not the same.
Measuring scales are different according to the degree of precision
involved. There are four types of scales of measurement.
1. Nominal:
• The simplest type of data, in which the values fall into un-
ordered categories or classes
• Uses names, labels or symbols to assign each measurement to
one of a limited number of categories that cannot be ordered.
• Examples: Blood type, sex, race, marital status, eye
colour, religious affiliation, survival status.

38. Cont.… • If nominal data takes only two possible values, they are called
dichotomous or binary.
• E.g. sex is dichotomous (male or female).
• Yes/no questions E.g., cured from TB at 6 months of Rx
2. Ordinal:
Assigns each measurement to one of a limited number of
categories that are ranked in terms of order.
• Although non-numerical, can be considered to have a natural
ordering
• Examples: Patient status, cancer stages, social class,
severity of pain, level of satisfaction, rating scale,

39. Summary of Nominal and ordinal
Marital status:
1. Single
2. Married
3. Widow
4. Divorce
♦ The numbers have NO
meaning
♦ They are labels only
♦ Pain level
1. None
2. Mild
3. Moderate
4. Severe
♦ The numbers have LIMITED
meaning 4>3>2>1 is all we know
apart from their utility as labels
E.g. Ordinal

40. 3. Interval scale:
- Used for quantitative variables
- assigns each measurement to one of an unlimited number of categories
that are equally spaced.
- Differences between any two numbers on a scale are of known size.
Example: Temp. in o
F on 4 consecutive days
Days: A B C D
Temp. o
F: 50 55 60 65
For these data, not only is day A with 50o F cooler than day D with 65o
but is 15o cooler.
- It has no true zero point. “0” is arbitrarily chosen and doesn’t reflect the
absence of temp.

41. 4. . Ratio scale:
- It is the highest scale of measurement used for quantitative
variables.
- Measurement begins at a true zero point and the scale has
equal space.
- Zero indicates the absence of the quantity being measured.
- Examples: Height, weight, Blood Pressure, time,
hospital length of stay, etc.
Someone who weighs 80 kg is two times as heavy as
someone else who weighs 40 kg.

42. Degree
of
precision
in
measuring
Nominal
Ordinal
Interval
Ratio

43. Summary
Operations that make sense
Scale Counting Ranking Addition
/Subtraction
Multiplication/
Division
Nominal ✓
Ordinal ✓ ✓
Interval ✓ ✓ ✓
Ratio ✓ ✓ ✓ ✓

44. Variable
Qualitative
or categorical
Quantitative
measurement
Nominal
(not ordered)
e.g. ethnic
group
Ordinal
(ordered)
e.g. response
to treatment
Discrete
(count data)
e.g. number of
admissions
Continuous
(real-valued)
e.g. height
Summary of Data

45. Exercise
Give the correct scales of measurement for each variable
1. Data on body temperature (Celsius) of a ICU patient
2. Hair colour
3. Weight of newly born child
4. Job satisfaction index (1-5)
5. Age of a cancer patient
6. Marital status of Hypertensive patients
7. Identification Number of a student
8. Class rank
9. Length of infants at ANC clinic
10. The average weight gain of children given with a special diet
11. Health status of a person
12. the net wages of dialysis patients;
13. The number of students in a college;
14. Times for swimmers to complete a 50-meter race

46. Exercise
1. Political party preference
2. Sex of a patient
3. Disease stage
4. Military status of a wounded military patient
5. IQ of a person
6. Weight of a kidney stone
7. Altitude measure of Addis Ababa
8. Patient’s hospital card number
9. A response to the statement "Abortion is a woman's right" where "Strongly
Disagree" = 1, "Disagree" = 2, "No Opinion" = 3, "Agree" = 4, and "Strongly
Agree" = 5, as a measure of attitude toward abortion.
10. Months of the year Meskerm, Tikimit…
11. Blood type of individuals, A, B, AB and O.
12. Regions numbers of Ethiopia (1, 2, 3 etc.)
13. Socioeconomic status of a family when classified as low, middle and upper classes.

47. • Data collection is a crucial stage in research.
• If the data collection has been superficial, biased
or incomplete, data analysis becomes difficult, and
the research report will be of poor quality.
• Therefore, we should concentrate all possible
efforts on developing appropriate tools, and
should test them for their validity
TECHNIQUES OF DATA COLLECTION

48. • The definition of observation is not limited to
‘watching’ but extended to the direct gathering of
information by the investigator using the senses,
generally both sight and hearing.
• Observation involves. systematically selecting,
watching, listening and recording behavior and
characteristics of the phenomena of interest.
• Observation of human behavior is a much-
used data collection technique
Observation

49. • Observations can give additional, more accurate information on
behavior of people than interviews or questionnaires
It can be undertaken in different ways;
Observations can also be made on objects; example, the
presence or absence of a latrine and its state of cleanliness may
be better assessed by observation.
Con…

50. • It is a data-collection technique that involves oral
questioning of respondents.
• Answers to the questions posed during an interview
can be recorded by writing them down (either during
the interview itself or immediately after the interview)
or by tape-recording the responses, or by a
combination of both.
• There are different types of interviews:
Structured
• closed questions; set order of questions
Interview (face-to-face)

51. Semi-structured
• Open and closed questions together or the fixed interview
guide approach where agenda set but open questions and
pre-determined questions.
In-depth (Unstructured)
• Issues covered in detail;
• respondent leads the interviews/sets the agenda;
• no fixed order of questioning..
• It is designed to allow the respondent to tell their story in their
own way, while ensuring that the aspects the researcher
wants to explore are covered.
• It also allows the subject matter to be explored in some depth
to discover the nature of the experience, feelings, and
perceptions of the respondent.
Con…

52. Characteristics of structured and unstructured
interviews
Structured interviews
• Asks each of the respondent same questions
using the same wording and typically has a
limited range of possible answers.
unstructured Interviews
• Allows the respondent to express their ideas in
their own way using their own words and
determining the range of aspects and issues they
want to raise.
Con…

53. Conducting an in-depth interview
• An interview guide is usually prepared.
An interview guide:
• Helps the interviewer to remember the points to cover.
• Suggests ways of approaching and talking about topics.
• Reminds the interviewer about probes and ways of asking
questions.
• Includes an introduction and way of ending the interview.
• Ensures that the interviewer covers all the topics.
• Gives a possible order of topics.
• Helps the interviewer to enable people to talk in their own
way
Con…

54. • is a data collection tool in which written questions
are presented that are to be answered by the
respondents in written form
• A written questionnaire can be administered in
different ways, such as by:
• Sending questionnaires by mail with clear
instructions on how to answer the questions and
asking for mailed responses;
• Gathering all or part of the respondents in one
place at one time, giving oral or written
instructions, and letting the respondents fill out the
questionnaires.
Self administer written questionnaire

55. • Hand-delivering questionnaires to respondents and
collecting them later
• The questions can be either open-ended or closed.
Example:
Closed end question
• What is the current breastfeeding status of mother?
1. Exclusive breastfeeding
2. Partial breastfeeding
3. Not breastfeeding
Open end question
• At what age should the child start supplementary food?
Con…

56. • It allows a group of 8 - 12 informants to freely
discuss a certain subject with the guidance of a
facilitator or reporter
Advantages:
• May encourage people to participate who
otherwise may not want to.
• Inter-interviewee ideas.
• Quick method for establishing parameters.
Disadvantages:
• Some topics may be too ‘sensitive’ and too
personal.
• Deviant views may be inhibited
Focus group discussions

57. Data collection
techniques
Data collection tools
Record review Checklist; data compilation forms
Observation Eye, and other sense organs, pen/paper,
watch, scales, microscope, etc
Interviewing Interview guide, checklist, questionnaire, tape
recorder.
Self administer
questionnaires
Questionnaire
Data collection techniques vs tools