This document provides an introduction to biostatistics. It defines key biostatistics terms like statistics, biostatistics, population, sample, parameter, and statistic. It also differentiates between descriptive and inferential statistics. Additionally, it discusses important biostatistics concepts like types of variables, levels of measurement scales, sources of data collection, and examples of each. The goal is to help learners understand the basic principles and applications of biostatistics in health research.

2. Introduction to Biostatistics
Shakir Rahman
BScN, MScN, MSc Applied Psychology, PhD Nursing (Candidate)
University of Minnesota USA
Principal & Assistant Professor
Ayub International College of Nursing & AHS Peshawar
Visiting Faculty
Swabi College of Nursing & Health Sciences Swabi
Nowshera College of Nursing & Health Sciences Nowshera
1

3. 3
Objectives
By the end of this session, the learners would be able to:
 Define the terms: Statistics and Biostatistics
 Discuss the importance of Biostatistics
 Differentiate between Population & Sample, Parameter &
Statistics
 Identify the various sources of data collection
 Explain the types of variables
 Explore the different types of Measurement scales

4. Imagine some action / activity done without the reference of
numbers

5. What if NUMBERS are not there in
life?
5

6. 6
A routine scenario in Nursing
Profession!
You,as a manager, have been requested by your
senior management to provide information about the
census of current Covid-19 cases in the hospital last
quarter (April-June,2022) in a 2 minutes presentation.
How would you respond to this request?

7. 7
Confirmed Covid Cases in last Quarters at
ABC Hospital in 2020
Wards No. of
patients
admitted
Less than
15
years
15-45 years More
than
45
years
Suspected
COVID
Cases
Confirmed
Cases
A 56 6 30 20 40 31
B 425 0 339 86 322 45
C 250 0 150 100 105 55
D 512 50 325 137 259 158
E 511 25 450 36 158 151
F 205 14 185 6 205 200
G 175 12 100 63 175 173
Total 2134 107 1579 448 1264 813

8. 6
• 2134 patients were admitted in ABC
Hospital the in last quarter. Analysis of
cases revealed that 1579 patients were
in the age group of 15-45 years, 448
were above 45 years while remaining
107 were below 15 years of age. Out of
1264 suspected cases, 813 confirmed
cases reported in last quarter
1 2
3
Suspected COVID Patients in last Quarter
2
% 13
%
4
%
10
%
6
%
8
%
7
%
50
%
Suspected COVID
Cases
A B C D E F Total
32
2
126
4
40 31 45 105
55
259
158 158
151
205
200
175
173
81
3
0
20
0
60
0
40
0
100
0
80
0
120
0
140
0
A B G Tota
l
C D
Suspected COVID
Cases
E F
Confirmed
Cases

9. • Tabular Presentation
• Textual Presentation
• Graphical Presentation
Methods ofPresenting the data

10. 10
Statistics
The science of data!
• Collection, Classification, Organization, Summarization,
analysis, Presentation, and Interpretation of the data / information.
• Statistics is science & art of dealing with variation in thedata
(information, facts) in such a way as to obtain reliableresults.

11. 11
Number of new polio cases in last 10 years in Pakistan
Positivity ratio of covid-19 in last 24 hrs.
Effect of iron supplements on HB levels of pregnant mothers
• Collection, Classification, Organization, Summarization,
Presentation, and Interpretation of the data / information.
• If related to Biological or Health sciences called“Biostatistics”
• Examples:
Biostatistics

12. 12
Why do we need to study Biostatistics
course?
Tolearn how to deal with numbers.
Toassess evidence from different studies.
Tounderstand published scientific papers.
T
o do research and write papers in
journals.
scientific

13. 13
Definitions
Population vs. Sample
• Population
– The set of all the measurements of interest to the
investigator.
• Monthly income of households in Pakistan
• Number of TB Patients inPakistan
• All the patients visited emergency of the ABC Hospital in the
year 2014
• Neonatal mortality in Pakistan

14. 14
Population vs. Sample
• Sample
– It is a group of subjects selected from a population
– A random sample is a good representative of
population
– Example
– A survey of 1,000 households taken from all parts
of Pakistan to assess their monthly income

15. 15
• Parameter
– The characteristics of interest to the researcher in
the population is called a parameter.
E.g. average household size and percent of
households with modern sanitation as reported in
the 1998 census of Karachi
• Statistic
– The characteristics of interest to the researcher in
the sub-set of population is called a statistic.
E.g. average household size and percent of
households as reported from a sample survey of
6,000 households in Karachi, 2010
Parameter vs. Statistics

16. Examples
Parameter:
• Average monthly income of households in
Pakistan
• Proportion of households in Karachi who have
at least one special child at their residence
• Prevalence of COVID 19 in Pakistan
Statistic:
If taken from a sample each one of above is
called statistic
16

17. Statistics
Descriptive Inferential

18. 18
Descriptive vs. Inferential Statistic
• Descriptive Statistic :
– Consists of the collection, organization,
summarization and presentation of data.
• Inferential Statistic :
– Consists of generalizing from samples to
populations, performing estimations and hypothesis
tests, determining relationships among variables

19. Inferential Statistics
Some research questions one would deal with using inferential
statistics are:
 How effective is a new vaccine against the COVID-19?
 How effective is a treatment that seeks to reduce the risk of
stroke?
 What is the prevalence of osteoarthritis in a rural community?

20. Statistics
Descriptive Inferential
Hypothesis
testing
Estimation
Point
Estimation
Interval
Estimation
20
Statistics

21. • A Variable is simply what is being observed or measured
–The dependent variable is the outcome of interest
–The independent variable is the intervention or what is
being manipulated
• Data
–The set of values collected for the variable of each of the
elements belonging to the sample
Data and Variables

22. 22
Source of Data Collection
Survey:
–Data are obtained by sampling some of the population of
interest. The investigator does not modify the environment.
–Example:
The Research health institute is interested in estimating the
prevalence of Vitamin D deficiency in rural and urban areas of
Pakistan.

23. Source of Data Collection
Experiment:
–The investigator controls or modifies the environment and
observes the effect on the variable under study.
–Example:
Cancer research institute is interested in determining the
effectiveness of a drug “X” for the treatment of cancer.

24. Quantitative
continuous
Types of variables
Quantitative variables Qualitative variables
Quantitative
discrète
Qualitative
nominal
Qualitative
ordinal

25. 3 - 25
There are two basic types of variables: Qualitative (categorical) and
Quantitative (Numerical)
Qualitative Variable:
Variables that can be placed into distinct categories, according to some
characteristic or attribute.
Cannot be measured on numeric/ quantitative scale.
Measured on qualitative scales i.e. Nominal & Ordinal Scales.
For example, gender (male or female), religious preference and
geographic locations.
Types of Variables

26. 3 - 26
Types of Variable
Quantitative variables
That have are measured on a numeric
or quantitative scale. Interval and ratio scales are quantitative
• Other examples are heights, weights, and body temperature.

27. 3 - 27
Types of Quantitative (Numerical) variables
It can be further classified into two groups: discreteand
continuous.
Discrete:
Discrete variables can be assigned values such as 0, 1, 2, 3 and
are said to be countable (Cannot be divided into fractions).
Examples: number of children in a family, number
of students in a classroom, and number of calls received by a
telephone operator each day for a month.
Continuous: Continuous variables, by comparison, can
assume an infinite number of values in an interval betweenany
two specific values(Can be divided into fractions).
Example: Vitamin D level, Hemoglobin levels, Serum
electrolyte levels, and Temperature etc.

28. Nominal Scale
- It is the first level ofmeasurement
- Named variables
- No ranking or order can be placed on thedata.
- Examples: Zip code, gender, causes of diseases/ conditions
Ordinal Scale
-Data measured at this level can be placed into categories, andthese
categories can be ordered, or ranked.
- For example, Grades of the students (A, B, C, D), Socioeconomic status (Poor,
Average, Rich), Performance appraisals (Average, Good, Very Good, Excellent)
Level of Measurement Scales

29. 3 -29
Level of Measurement Scales
Interval scale:
 Differences between values have meaning.
 Ordered with proportionate difference between variables
 Arbitrary Zero (0 will have a meaning)
 IQ is an example of such a variable. There is a
meaningful difference of 1 point between an IQ of 109
and an IQ of 110.
 There is an arbitrary zero (zero has some value, no true 0)
For example, IQ tests do not measure people who have no
intelligence. For temperature, 0 F does not mean no heat at
all.

30. 3 -30
Level of Measurement Scale
Ratio scale:
 Differences between values have meaning.
 Ordered with proportionate difference between
variables
 Absolute Zero (0 means absence of characteristics)
 Examples:
 Age, Height, Weight, No. of children, Rates (Blood
Pressure, Pulse Rate, Respiratory rate) etc.

32. Levels of Measurement Scales
3 - 32

34. Identify the type of Scale
Number of patients coming to a clinic perday.
Smoker or not (1.Yes 2.No)
Daily temperature
Pain score on a scale of 0 to 10
Medical record number
Classification of children in a day care centre(infant,
toddler, pre school)
Have you heard of night blindness? (1. Yes 2.No)

35. 34

36. Acknowledgments
Dr Tazeen Saeed Ali
RM, RM, BScN, MSc ( Epidemiology &
Biostatistics), Phd (Medical Sciences), Post
Doctorate (Health Policy & Planning)
Associate Dean School of Nursing & Midwifery
The Aga Khan University Karachi.
Kiran Ramzan Ali Lalani
BScN, MSc Epidemiology & Biostatistics
Aga Khan University Karachi

38. References
Brais, B., Xie, Y. G., Sanson, M., Morgan, K.,
Weissenbach, J., Korczyn, A. D., ... & Rouleau, G.
A. (1995). The oculopharyngeal muscular
dystrophy locus maps to the region of the cardiac
α and β myosin
heavy chain genes on chromosome 14q11.
2− q13. Human molecular genetics, 4(3),
429-434.