Basic Concepts of Inferential Statistics

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Basic Concepts of
Inferential Statistics

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WHAT IS INFERENTIAL
STATISTICS?
Inferential statistics is a technique used to
draw conclusions about a population by
testing the data taken from the sample of
that population.
 It is the process of how generalization from
sample to population can be made. It is
assumed that the characteristics of a sample
is similar to the population’s characteristics.
 It includes testing hypothesis and deriving
estimates.
 It focuses on making statements about the
population.


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THE PROCESS OF INFERENTIAL
ANALYSIS

Raw
Data

• It comprises of all the data collected from the
sample.
• Depending on the sample size, this data can be
large or small set of measurements.

• It summarizes the raw data gathered from the
sample of population
Sample •
Statistic These are the descriptive statistics (e.g. measures
of central tendency)
s

Inferenti • These statistics then generate conclusions about
the population based on the sample statistics.
al
Statistic
s

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SAMPLING METHODS
Random sampling is the best type of sampling
method to use with inferential statistics. It is also
referred to as probability sampling.
 In this method, each participant has an equal
probability of being selected in the sample.
 In case the population is small enough then
everyone can be used as a participant.

Another sampling technique is Snowball
sampling which is a non-probability sampling.
 Snowball
sampling
involves
selecting
participants on the basis of information provided
by previously studied cases. This technique is
not applied for inferential statistics.


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IMPORTANT DEFINITIONS
Probability is the mathematical possibility that a
certain event will take place. They can range from
0 to 1.00
 Parameters describe the characteristics of a
sample of population. (Variables such as age,
gender, income, etc.).
 Statistics describe the characteristics of a sample
on the same types of variables.
 Sampling Distribution is used to make inferences
based on the assumption of random sampling.


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SAMPLING ERROR CONCEPTS
Sampling Error: Inferential statistics takes
sampling error (random error) into account. It
is the degree to which a sample differs on a
key variable from the population.
 Confidence Level:
The number of times out of 100 that the true
value will fall within the confidence interval.
 Confidence Interval:
A calculated range for the true value, based
on the relative sizes of the sample and the
population.
 Sampling error describes the difference
between sample statistics and population
parameters.


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SAMPLING DISTRIBUTION
CONCEPTS

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TYPES OF HYPOTHESES


Alternative
hypothesis:
It
specifies
expected relationship between two or more
variables. It may be symbolized by H1 or Ha.

Null hypothesis: It is the statement that
says there is no real relationship between the
variables described in the alternative
hypothesis.
 In inferential statistics, the hypothesis that is
actually tested is the null hypothesis.
Therefore, it is essential to prove that the null
hypothesis is not valid and alternative
hypothesis is true and should be accepted.


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HYPOTHESIS TESTING PROCESS

State the
research
hypothesis

State the null
hypothesis

Make a decision
regarding
whether to
accept or reject
the null
hypothesis.

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Choose a level
of statistical
significance

Select and
compute the
test statistic

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