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STATISTICS
INTRODUCTION AND
DEFINITIONS
Public health
(Community Medicine)
• Is the art & science of preventing diseases,
promoting health & prolonging life through
the organized efforts of society.
• It deals with the health of the whole
population.
• Public health professionals with the aids of
Public health programmes, work to improve
the health of the population.(=Community )
2
Public health
(Community Medicine)
• Looks at the community (population)
it self as a patient.
• Deals with the community as a social
system and with the structure,
function & dysfunction of such
system.
3
Scope of Community Medicine
• Epidemiology of infectious diseases and
chronic diseases,
• statistics,
• school health,
• mental health,
• maternal and child health (MCH),
environmental health,
• rural health, urban health and
• occupational health
Clinician versus community
physician (GP, FP):
• A clinician's target is to diagnose and treat
his patient,
• while a community physician's target is to
manage health problem in a community
(i.e. Community diagnosis).
• Investigations in community medicine
follow the sequence of; observation,
hypothesis formulation, hypothesis testing,
data collection and analysis, results
interpretation and conclusions.
STATISTICS
 A field of study concerned with methods
and procedures of:
Collection, Organization, Classification,
Summarization ( Descriptive Statistics) ,
Analysis, and Drawing of inferences
( Analytic Statistics) about a body of data
when only part of it is observed .
BIOSTATISTICS
When the data are derived from biological
and medical sciences
PURPOSES of Statistics
1. Data reduction(sumerization) thus facilitating
interpretation.
2. To see the effect of a certain event is a real
one or arise from chance fluctuation because
of error in the sample of subjects (Analysis) .
3.Sampling and generalization .
DATA
Are raw material of statistics (may be
numbers or not) ,it comprise observations
on one or more variable.
 The VARIABLE : A characteristic that
takes different values in different persons,
places, things,…
I. QUANTITATIVE VARIABLE
 The variable that can be measured in the
usual sense of measurement as age ,
weight, height,…
So it is measurable.
We have 2 subtypes of quantitative v.
1. Discrete variable
2. Continuous variable
DISCRETE VARIABLE
It is characterized by gaps or interruptions
in the values (integer values) that can be
assumed as number of admissions,
decayed teeth,…
So for example we can not say that we
have 2.5 decayed teeth (it is either exactly
2 or 3).
CONTINOUS VARIABLE
It does not posses the gaps or interruption
characteristic of discrete variable as
weight, height, mid-arm circumference,…
Or it is infinite continuum of possible
values.
II. QUALITATIVE VARIABLE
It is the variable that can not be measured
in the usual sense but can be described.
In this case we count the number of
individuals falling into each category
(frequency) as the socioeconomic status,
diagnostic category,…
II. QUALITATIVE VARIABLE
1. NOMINAL Variable
It uses names, numbers or other
symbols. Each measurement assigned to
a limited number of unordered categories
and fall in only one category
• For example the gender either male or
female.
II. QUALITATIVE VARIABLE
2. ORDINAL Variable
• Each measurement is assigned to one of
a limited number of categories that are
ranked in a graded order.
• Differences among categories are not
necessary equal and often not
measurable
• For example the stage of a cancer may
fall in stage 1, 2 or 3 according to the
progression of the disease.
Types of Variables
Derived data
• We may encounter a number of other
types of data in the medical field.
• These include:
1. Ratios
2. Proportions( as 0.3) and percentages (30%)
3. Rates
Ratio
• Used to compare two quantities
• Usually the numerator is not a component
of the denominator
Commonly used ratios:
Ratio of female to male births
Maternal mortality ratio
• The ratio of people with tuberculosis to those
without tuberculosis.
Proportion and Percentage
• A specific type of ratio in which the
numerator is included in the denominator,
usually presented as a percentage
Calculation of proportion:
Number of infants who are immunized in Dywaniya
Total eligible infants for immunization in Dywaniya
55,000
 73.3%
75,000
Rate
• Special form of proportion that includes a
specification of time
• Most commonly used in epidemiology
because it most clearly expresses probability
or risk of disease or other events in a defined
population over a specified period of time
POPULATION
• POPULATION OF ENTITIES
Largest collection of entities that had
common characteristics for which we have
an interest at one particular time
• POPULATION OF VARIABLES
It is the largest collection of values of a
random variable for which we have an
interest at a particular time
SAMPLE
• It is part or subset of the population
• Sample of entities, which is a subset of
population of entities
• Sample of variables which is subset of
population of variables
DEFINITIONS
 Statistical
inference: is a conclusion
concerning a population of observations
made on the basis of the results of
studying a sample of these observations
DEFINITIONS

Sampling Error: it is the difference between a
sample measure and its corresponding
population measure.
Sampling error is not a mistake, but it is a
calculated error that should be quantify
DEFINITIONS

Sampling frame: It is a numerical list of all the
units composing the study population
i.e. every member of the population has a
unique identification number
TYPES OF SAMPLING METHODS
Probability sampling:
The results of studying the sample are
generalizable to the underlying population from
which this sample had been drawn
 Non-probability sampling:
The results of studying the sample can not be
generalized to the underlying population from
which this sample had been drawn

PROBABILITY SAMPLE

The sample is drawn from the population in
such a way that every member of the
population has the same probability (chance) to
be included in the sample
TYPES OF PROBABILITY SAMPLES
A. Simple random sample:
It requires:
 Sample frame: a numerical list of all observations
(or units) composing the population
 Sample fraction: sample size to the total
population
 The sample is selected by use of: Lottery method,
computer or by random number table
B. SYSTEMATIC SAMPLE
Choosing units or observations from the
sample frame at regular interval (every nth)
To find the “system” we divide the population
size by the required sample size
SYSTEMATIC SAMPLE
e.g. if the population is composed of 1200
units & we want to select a sample of 100, we
can use the interval:
1200/100=12
So we will choose every 12th
The starting point can be chosen at random like
2, so the selected units will be 2, 14, 26 ,….
LIMITATION

Simple random sample & systematic sample
can't ensure that the structure of the sample
will be similar to the structure of the underlying
population regarding certain characteristics, as
age , sex,…
C. STRATIFIED SAMPLE
The sample frame is divided into strata (or
groups) according to certain characteristic (s),
and then a simple random or systematic
sampling will be applied on each stratum.
The number of units included in the sample
from each stratum can be achieved by:
 Equal
allocation from each stratum
 Proportional allocation:
The number from each stratum is proportional to
the size of the stratum, this is possible only if we
know the proportion of population in each stratum
to the whole study population
D. CLUSTER SAMPLE:



The selection here will be of groups of units
rather than individual units
A sample frame of groups of study units
(cluster) should be available, then a random
sample of these clusters will be chosen.
Cluster could be schools, clinics, hospitals,
villages, factories,…..
E. MULTI-STAGE SAMPLE



This procedure is carried out in phases
(stages).
It can involve more than one of the above
sampling method.
It is used for very large population & when the
sample frame is not available for the whole
population
NON- PROBABILITY SAMPLING TYPES:

Convenience sample:
The study units that happen to be present at
the time of data collection will be included in
the sample. This is not representative to the
population we want to study
NON- PROBABILITY SAMPLING

Quota sample:
The composition of the sample regarding
certain characteristic and the number of units
having these characteristics are decided from
the beginning, and the only requirement is to
find the people fitting these quotas
NON- PROBABILITY SAMPLING

The results of non probability sample are valid
(to the studied sample) but are not
generalizable to the underlying population

So non probability sample are inappropriate if
the aim is to generalize finding obtained from
the sample to the total study population
ADVANTAGES OF COLLECTING INFORMATION
USING SAMPLES:
Sampling may be the only feasible method of
collecting the information
 Sampling reduce demand on resources
(finance, personnel, materials)
 Results are obtained more quickly
 Sampling may lead to better accuracy of
collected data
 Precise allowance can be made for sampling
errors

DISADVANTAGES OF COLLECTING INFORMATION
USING SAMPLES:

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There is always sampling error
Sampling may create a feeling of
discrimination within the population
Sampling may be inadvisable when every unit
in the population is legally required to have a
record
For rare events, small samples may not yield
sufficient cases for study