Statistics is the science of collecting, organizing, and analyzing data to draw conclusions. There are two main types of data: data from measurements and data from counts. Data can come from various sources like records, surveys, experiments, and external reports. Biostatistics analyzes data from biological sciences and medicine. Variables are characteristics that can take different values and are either quantitative (measured) or qualitative (categorical). Variables can be random, continuous, discrete, independent, or dependent. Samples are subsets of populations used for statistical analysis. Common random sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

1. Bio-Statistics
Prepared by:
Assistant Prof.
Namir Al-Tawil

2. Definition of statistics
It is the science that is concerned
with collection, organization,
summarization, and analysis of
data; then drawing of inferences
about a body of data when only a
part of data is observed.

3. Data
 Are the raw material of statistics.
 Simply defined as numbers.
 Two main kinds of data:
– Result from measurement (such as body
weight).
– Result from counting (such as No. of
patients discharged).
 Each No. is called datum.

4. Sources of data
 Routinely kept records. E.g.: hospital
medical records.
 Surveys.
 Experiments.
 External sources. E.g.: published
reports, data banks, research
literature.

5. Definitions:
 Biostatistics:
A term used when the data analyzed are
derived from biological sciences and
medicine.
 Variable:
The characteristic takes different values in
different persons, places or things, so we
label a characteristic as variable. E.g. :
blood pressure, weight, height,

6. Definitions:
 Quantitative variable
A variable that can be measured in the usual
sense. E.g.: Weight of pre-school children,
age of patients ……
 Qualitative variable
Can not be measured as the quantitative
variable, e.g. ethnic group, possessing a
characteristic or not such as smokers and
non-smokers. Here we use frequencies
falling in each category of the variable.

7. Classification of variables:
Random variable :
Results only by chance factors i.e. can not be
predicted.
I. Classification based on GAPPINESS
 Continuous random variable
Does not possess gaps. E.g. height and weight.
 Discrete random variable
Characterized by gaps or interruptions in the
values that it can assume. E.g. No. of admissions
per day, or No. of missing teeth.
 Categorical (e.g. sex and blood groups).
 Numerical discrete (No. of episodes of angina).

8. Classification, cont.
Note:
To summarize discrete variables we measure the proportion of
individuals falling within each category. For continuous
variables we need measures of central tendency and measures
of dispersion.
II.Classification by DESCRIPTIVE ORIENTATION
 Independent variable:
Is a factor that we are interested to study. E.g. meat
intake in grams per day.
 Dependent variable (outcome variable):
Is the factor observed or measured for different
categories of the independent variable. E.g.
hypercholesterolemia.

9. III. Classification by levels of
measurement
The nominal scale: Consists of
classifying the observations into
various mutually exclusive categories.
E.g. males & females.
The ordinal scale: Observations are
ranked according to some criterion,
e.g. patients status on discharge
from hospital (unimproved, improved,
much improved).

10. Levels of measurements, cont.
The numerical scale
Sometimes called quantitative observations.
There are two types of numerical scales:
1.Interval or continuous scales e.g. age.
2.Discrete scales (e.g. No. of pregnancies).
Means and standard deviations are generally
used to summarize the values of numerical
measures.

11. Definitions
Population:
The largest collection of entities
for which we have an interest at
a particular time.
Sample:
Part of a population.

12. Random (probability) Sampling
methods
1. Simple random sampling:
Use random number table.
(see next slide).

14. Random (probability) Sampling
methods
2. Systematic sampling: Include individuals
at regular intervals. E.g. individuals No. 4,
7, 10, 13, …. Will be included.
The interval in this example is (3), measured
by dividing the No. of the population by
the required sample. E.g. 60/20.
The starting point must be chosen randomly.

15. Random (probability) Sampling
methods
3. Stratified sampling: Divide into
subgroups according to age and sex for
example, then take random sample.
4. Cluster sampling:
It results from 2 stage process. The
population is divided into clusters, and a
subset of the clusters is randomly
selected.
Clusters are commonly based on geographic
areas or districts.

16. Convenience sampling
Note: It is not always possible to
take a random sample, e.g. a busy
physician who wants to make a
study on 50 patients attending
the out-patient clinic. This is
called a convenience sampling
(non random).