What is statistics?
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STATISTICS
COLLECTION
ORGANIZATION
PRESENTATION
ANALIZATION
INTERPRETATION
BIOSTATISTICS
It is a branch of applied statistics
that concerns the application of statistical
methods to medicine and biological
problems.
DIVISION OF STATISTICS
DESCRIPTIVE STATISTICS
It deals with the collection, organization,
presentation and computation of data to
describe the samples under investigation.
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Examples of Descriptive Statistics
Results presented in a medical
record of a patient.
Some investigators have proposed
that consumption of Vitamin A
prevents cancer.
The chance a new born baby is
female is slightly less than 50%
INFERENTIAL STATISTICS
It gives information, inferences, and
implications regarding the population by
studying its representative samples.
Examples of Inferential Statistics
 Smoking increases the risk of lung
cancer.
 Majority who died of lung cancer and
liver cancer are males.
 Drinking decaffeinated coffee can
raise choleste.rol levels by 7%
 Population and Sample
 Entire group of scores
Score of students in a class
 All children of any age who have
older or younger siblings
 Study on siblings, the 40 children
who actually participated in one
specific study .
What is variable?
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Example of Variables
Gender
Age
Religion
Blood Type
Number of siblings
Population
Marital status
Height
Student number
Number of patients
Score
SAMPLING TECHNIQUES
It refers to the process of selecting the
subjects who will participate in a research
study.
Probability sampling
Simple Random Sampling
The basic type and most popular sampling
design. This is one in which each member
of the population has an equal chance of
being chosen.
Systematic sampling
It involves selecting every nth element in the
population until the desired number samples
is obtained.
To find the nth element ,
𝑛^𝑡ℎ=𝑁/𝑛
STRATIFIED SAMPLING
This is the process of subdividing the
population into subgroups and drawing
members at random from each group in the
same proportion as they exist in the
population.
Steps on stratified sampling:
1. Identify the population
2. determine the sample size
3. Identify the strata
4. determine the number of respondents
to be selected from each stratum
Cluster sampling
The selection of groups or clusters of
subjects rather than individuals. This
sampling design is used when the
population is very large and widely spread
out over a wide geographical area.
Multi - stage sampling
This design is an extended version of
cluster in sampling. The population units are
grouped in hierarchy of elements and
sampling are done successively.
Non – probability sampling
COVENIENCE SAMPLING
This design remains resorted to when it is
extremely difficult to select a random
sample. Thus, a researcher simply takes the
closest persons who are available for the
study.
PURPOSIVE SAMPLING
This design is also known as judgemental
sampling. A purposive sample is selected
because the individuals have special
qualifications of some sort.
SNOWBALL SAMPLING
This design requires identification of a few
persons whose qualifications meet the
purposes of the study. These persons serve
as informants leading the researcher to
other individuals who qualify for inclusion in
the sample.