Inferential statistics is a method used to make generalizations about a population based on sample data, focusing on hypothesis testing and deriving estimates. Key components include sampling methods, the impact of sampling error, and the differences between null and alternative hypotheses. The validity of the null hypothesis is tested to confirm or support the alternative hypothesis.

1. Basic Concepts of
Inferential Statistics
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2. 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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3. 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.
Sample
Statistics
• It summarizes the raw data gathered from the sample of
population
• These are the descriptive statistics (e.g. measures of central
tendency)
Inferential
Statistics
• These statistics then generate conclusions about the
population based on the sample statistics.
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4. 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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5. 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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6. 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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7. SAMPLING DISTRIBUTION CONCEPTS
The variables of a
sample taken
from the
population
should be the
same for the
population also.
Due to sampling
error, the sample
mean can be
varied.
The amount of
this variation in
the sample mean
is referred to as
standard error.
Standard error
decreases as the
sample size
increases.
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8. 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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9. HYPOTHESIS TESTING PROCESS
State the research
hypothesis
State the null
hypothesis
Choose a level of
statistical
significance
Select and
compute the test
statistic
Make a decision
regarding whether
to accept or reject
the null hypothesis.
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