Dr.Thet Aung Moe( Roll NO- 29 )
Dr. Win Thandar Phyu ( Roll No-27)
(1/2015) Diploma In Hospital Administration
A field of study concerned with (1) the
collection, organization, summarization &
analysis of data ; & (2) drawing of inferences
about a body of data when only a part of the
data is observed .
The science of collecting , organizing,
presenting , analyzing & interpreting data to
assist in making more effective decisions.
Method of organizing, summarizing & presenting data in an informative
way. (eg.Mean BP of patients)
The method used to find out something about a population , based on a
sample .( eg.Mean BP of all adults in a city )
Statistics concerned with biological sciences & medicine.
population based on
data observed in a
4. Presenting Data
1. Making Inference
2. Hypothesis Testing
4. Making Predictions
Statistical inference is the procedure by which
we reach a conclusion about a population on the
basic of the information contained in a sample that
has been drawn from that population.
Consist of generalizing from
samples to population,
performing estimation &
hypothesis test ,determining
relationship among variables, &
•To describe the chance of an
•Final result will be in the form
Predicting effectiveness of a drug , predicting the relationship between
death & smoking habit
Inferential Statistics :
parameters based on
samples of data.
characteristic of a
Measurement & Data Types are important in
Largest collection of entities or values of a random variable
for which we have an interest at a particular time.
( eg. weights of infants , weights of adults )
Finite population – fixed numbers of values
(eg. Adult population in a village)
Infinite population – endless succession of values
(eg.OPD patients in a hospital)
Sample : A part of population
There are many kinds of samples that may be drawn
from a population.
Not every kind of sample can be used as a basis for
making valid inferences about a population.
To make a valid inference about a population , we need a
scientific sample from the population.
The simplest of these scientific samples is the Simple
Ifwe use the letter N – size of a finite population
The letter n - size of a sample , we defined a simple random sample.
If a sample of size “n” is drawn from a population of size
“N” in such a way that every possible sample of size “n” has
the same chance of being selected , the sample is called a
simple random sample.
137 1 43
114 2 66
115 3 61
183 4 64
185 5 65
028 6 38
085 7 59
181 8 57
018 9 57
164 10 50
Sample of 10 Ages drawn from the ages
The mechanics of drawing a simple random
sample is called simple random sampling.
May be with replacement or without
As a rule in practice sampling is always done
without replacement .
Eg. A simple random of size n=10 from a
population of size N = 189*
Random sampling is important role in designing research studies &
A research study is a scientific study of phenomenon of interest .
Research studies involve sampling protocols , collecting & analyzing
data, & providing valid conclusions based on the results of the
Experiments are a special type of research study in which
observations are made after specific manipulations of conditions
have been carried out ; they provide the foundation for scientific
A sampling method that is widely used in
healthcare research is the systemic sample .
Medical records which contain raw data used
in healthcare research are stored in a file
system or a computer.
Are easy to select in a systematic way.
Stratified Random Sampling
A method of sampling that involves the divisions of
a population into smaller groups known as strata . In
stratified random sampling, the strata are formed
based on member’s shared attributes or
A random sample from each stratum is taken in a
number proportion to the stratum ‘s size when
compared to the population.
These subsets of the strata are pooled to form a
Two Main Methods Used In Inferential Statistics
1. Estimation: A sample is used to estimate a
parameter & confidence interval about the estimate
is constructed .
2. Hypothesis : When doing an experiment we
assume an hypothesis is called Null Hypothesis. Data
are collected & the null hypothesis is compared with
If the data are very different from the null hypothesis
it is rejected.
If not, it is accepted.