Populations and samples

1. Amity Institute of Information Technology
Introduction to Data Science
BSc.IT/BCA/ DUAL VI Semester
Faculty: Dr. Shambhu Kumar Jha
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Module I
Introduction to Statistical Inference
Populations and samples
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What is Statistical Inference?
• Statistical inference is the process of drawing conclusions
about an underlying population based on a sample or
subset of the data.
• Statistical procedures use sample data to estimate the
characteristics of the whole population from which the
sample was drawn.
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What is Statistical Inference?
• Data Scientists typically want to learn about a population.
• While studying a phenomenon, such as the effects of a new
medicine at a population level is much more valuable than
understanding only the few participants in a study.
• Unfortunately populations are usually too large to measure
fully.
• Researchers must use a manageable subset of that
population to learn about it.
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Statistical Inference?
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What is Statistical Inference?
Example: Imagine that you are studying a new medication.
 As a scientist, you’d like to understand the effect of
medicine in the entire population rather than just a small
sample.
 After all, knowing the effect on a handful of people isn’t
very helpful for the larger society!
• Consequently, you are interested in making a statistical
inference about the medicine’s effect in the population.
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How to make Statistical Inference?
Statistical inference requires using specialized sampling methods
that tend to produce representative samples.
If the sample does not look like the larger population you’re
studying, you can’t trust any inferences from the sample.
Consequently, using an appropriate method to obtain your sample is
crucial.
The best sampling methods tend to produce samples that look like
the target population.
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Populations and samples
• A population is the entire group that you want to draw
conclusions about.
• A sample is the specific group that you will collect data from.
• The size of the sample is always less than the total size of the
population.
• In research, a population doesn't always refer to people.
• Data sampling is a statistical analysis technique used to select,
manipulate and analyze a representative subset of data points to
identify patterns and trends in the larger data set being
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Populations and samples
• What is mean by population in data science?
In statistics, population is the entire set of items from which you
draw data for a statistical study. It can be a group of individuals, a
set of items, etc. It makes up the data pool for a study. Generally,
population refers to the people who live in a particular area at a specific
time.
• Why do we use samples instead of populations?
• It is efficient: When a sample is studied, instead of a whole
population,
• it is a much quicker process and is more time efficient.
• It is practical: Most studies aim to make inferences about large
populations.
• These populations are too large to collect data from each element
within them.
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Populations and samples
What are the 4 types of population?
• They are:
• Finite Population.
• Infinite Population.
• Existent Population.
• Hypothetical Population.
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What are the 4 types of population?
• Finite Population: Population is considered finite if it is
small. possible to count its individuals without difficulty.
Example: population of ducks in one cage, the number of A class
students, the male population in an environment, and so on.
• Infinite Population: Infinite population is also known as an
uncountable population in which the counting of units in the
population is not possible.
Example: Number of germs in the patient's body is uncountable.
• Existent Population: The population which comprises of objects that
exist in reality is called existent population.
Examples are books, students
• Hypothetical Population: The hypothetical population is the
one which its population come from imaginary measurements.
• The world population is hypothetical. 13

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Sampling and its Advantage
• Sampling is a technique of selecting individual members or a
subset of the population to make statistical inferences from them
and estimate the characteristics of the whole population.
• Different sampling methods are widely used by researchers
in market research so that they do not need to research the entire
population to collect actionable insights.
• It is also a time-convenient and a cost-effective method and hence
forms the basis of any research design. Sampling techniques can be
used in a research survey software for optimum derivation.
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Types of sampling: sampling methods
Sampling is of two types:
• Probability sampling and
• Non-probability sampling.
Probability sampling: Probability sampling is a sampling technique
where a researcher sets a selection of a few criteria and chooses
members of a population randomly. All the members have an equal
opportunity to be a part of the sample with this selection parameter.
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Advantages of probability sampling
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Types of sampling: sampling methods
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Non-probability sampling: In non-probability sampling, the
researcher chooses members for research at random. This sampling
method is not a fixed or predefined selection process. This makes it
difficult for all elements of a population to have equal opportunities to
be included in a sample.
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Non-probability sampling:
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