INFERENTIAL STATISTICS
(handout 1)
Statistics – the science of collecting, presenting,
interpreting, and analyzing data for
effective decisions
Definition of terms:
Population – the entirety or totality of objects,
things or persons under study
Sample – a subset taken from the population
Parameter – numerical measures or characteristics
of the population
Statistic – numerical measure or characteristic of
the sample
Variable – a characteristic or property whereby the
members of the group differ from one another
Constant - a characteristic or property whereby the
members of the group do not differ from one
another
Uses of Statistics:
1. It can give precise description of the data
2. It can predict the behavior of individuals
3. It can be used to test the hypothesis
4. It can be used in decision making in any field
Misuses of Statistics:
1. Suspect samples
- use very small sample to obtain information
2. Ambiguous averages
- Common measures that are loosely called
averages are mean, median, mode and
midrange
-can differ from the data set
3. Changing the subject
- Different values are used to represent the
same data
4. Detached Statistics
- One in comparison with no comparison is
made
5. Implied connections
- Attempt to imply connections between
variables that may not actually exist
6. Misleading graphs
-Graphs give a visual representation of data
that enables the viewers to analyze and
interpret data more easily than by simply
looking at numbers
-Can misrepresent the data and lead the reader
to false conclusions
7. Faulty survey questions
- Be sure that questions are properly written
since the way questions are phrased can often
influence the way people answer them
Types of Statistics as to:
a. Nature
a.1 qualitative – non-numeric data
ex. Attitude, gender, religion, ID number
a.2 quantitative
ex. Age, weight, height, IQ
b. Characteristics (quantitative data)
b.1 discrete – whole numbers(countable)
ex. no. of boys in a class, pieces of
shampoo sachets in a box
b.2
continuous –
may be
(measurable)
ex. age, length, monthly revenue
decimals
c. Sources
c.1 primary
ex. personal interview, telephone survey
c.2 secondary
ex. books, report cards
Levels of measurement
a. Nominal – data that consists of names, labels,
classification and categories only
- Data cannot be arranged in an ordering scheme
ex. gender, ethnicity, religion
b. Ordinal – data may be arranged in some order
but differences between data values cannot be
determined or meaningless
ex.
performance
rating(O,VS,S,F,P),
size(S,M,L) winners in a contest, level of
efficacy(VE, E, ME, LE, NE)
c. Interval – same as the ordinal, with additional
property that we can determine meaningful
amounts of differences between the data
- No true zero point
ex. scores, temperature, IQ
d. Ratio – an interval level modified to include the
inherent zero starting point
- Highest level of measurement
Ex. age, weight, height, salary
Classification of Statistics
1. Descriptive Statistics – a manner of
organizing, presenting or summarizing a set of
data or observations in an informative way
Ex.
a.) the average age of CTU 3rd year
College students is 19 yrs old
b.) The dropout rate of CTU-COED students
c.) The adviser posted the top 10 students of
his class
2. Inferential Statistics – uses sample data to
make inferences about a population
- Consists of generalizing from samples to
populations, performing hypothesis testing,
determining relationships among variables,
and making predictions
Ex.
a.) the researcher tests the relationship
between gender and academic performance
of the students in Math
b.) the researcher tested the significance on
the difference of the opinion of the people
from Visayas and Mindanao regarding the
imposition of Martial Law in Mindanao
c.) DENR forecasted that the Philippine forests
will be 65% denuded in 10 years if illegal
logging will not stop
d.) The researcher studied the effect of
reporting to students’ learning
SAMPLING AND SAMPLING TECHNIQUES
SAMPLING AND SAMPLING TECHNIQUES
Sampling is the act, process, or technique of
selecting an appropriate sample, or representative
part of a population for the purpose of determining
the characteristics of the whole population.
Sampling is the act, process, or technique of
selecting an appropriate sample, or representative
part of a population for the purpose of determining
the characteristics of the whole population.
Reasons For Sampling
a. the destructive nature of the objects or things
under study
b. the population under study is too large
Reasons For Sampling
a. the destructive nature of the objects or things
under study
b. the population under study is too large
Two Major Types Of Sampling
1. Random Sampling/Scientific Sampling - a
process whose members have an equal chance
of being selected from the population; it is also
called probability or scientific sampling.
2. Non-Random
Sampling/Non-Scientific
Sampling - a sampling procedure where
samples selected in a deliberate manner with
little or no attention to randomization; it is also
called non-probability or non-scientific
Two Major Types Of Sampling
1. Random Sampling/Scientific Sampling - a
process whose members have an equal chance
of being selected from the population; it is also
called probability or scientific sampling.
2. Non-Random
Sampling/Non-Scientific
Sampling - a sampling procedure where
samples selected in a deliberate manner with
little or no attention to randomization; it is also
called non-probability or non-scientific
sampling.
Types Of Random Sampling
sampling.
Types Of Random Sampling
1. Simple Random Sampling is a process of
1. Simple Random Sampling is a process of
Types Of Non-Random Sampling
Convenience
Sampling/haphazard
sampling.
- samples taken are readily available to
participate in the study.
Purposive Sampling - sampling is done with
a purpose in mind. This technique, also called
judgmental or selective sampling, focuses on
samples which are taken based on the
judgment of the researcher. The goal of this
sampling is to carefully choose the members of
the population which are best fitted to answer
the research questions.
Quota sampling is the equivalent of stratified
random sampling in terms of non-probability
sampling.
Snowball sampling is sometimes called chain
– referral sampling. In this technique, the
researcher chooses a possible respondent for
the study at hand. Then, each respondent is
asked to give recommendations or referrals to
other possible respondents.
Types Of Non-Random Sampling
Convenience
Sampling/haphazard
sampling.
- samples taken are readily available to
participate in the study.
Purposive Sampling - sampling is done with
a purpose in mind. This technique, also called
judgmental or selective sampling, focuses on
samples which are taken based on the
judgment of the researcher. The goal of this
sampling is to carefully choose the members of
the population which are best fitted to answer
the research questions.
Quota sampling is the equivalent of stratified
random sampling in terms of non-probability
sampling.
Snowball sampling is sometimes called chain
– referral sampling. In this technique, the
researcher chooses a possible respondent for
the study at hand. Then, each respondent is
asked to give recommendations or referrals to
other possible respondents.
selecting n sample size in the population. It is
the most commonly used sampling technique.
2. Systematic Sampling is a random sampling
technique which considers every
element
of the population in the sample with the
selected random starting point from the first
members
3. Stratified Sampling - a given population is
purposively divided into homogenous partitions
(or groups) depending on certain factors that
might be affecting the results of the study.
These homogenous partitions are also called
strata
4. Cluster Sampling - grouping the respondents
according to their geographical location
1.
2.
3.
4.
selecting n sample size in the population. It is
the most commonly used sampling technique.
2. Systematic Sampling is a random sampling
technique which considers every
element
of the population in the sample with the
selected random starting point from the first
members
3. Stratified Sampling - a given population is
purposively divided into homogenous partitions
(or groups) depending on certain factors that
might be affecting the results of the study.
These homogenous partitions are also called
strata
4. Cluster Sampling - grouping the respondents
according to their geographical location
1.
2.
3.
4.