# Population And Samples In Educational, Psichology, Social And Scientific Research

**Population
And Samples Research**

*Populations*

*Populations*

A population is a group of people who have one or more characteristics and are of
interest to the researcher. As described below, there are several ways to
configure a population depending on the characteristics of interest.

The main objective of this study is to find principles that can be applied
universally, but examining the entire population to arrive at generalizations
would be impractical, if not impossible. The population is defined as a group of
people who have at least one common characteristic that differentiates this
group from others. For example, we may want to examine people with learning
difficulties.

It
are a large population and the people in it differ in many other characteristics
such as age, level of education, etc. Therefore, it is not possible to study
this population because of its size and not very useful because of its
diversity.

To
solve this second problem, which is too diverse, we need to narrow the
population and perhaps only study third graders with learning disabilities who
also, attend the large public school system. This will generate a target audience consisting of a specific group for which we want to summarize the
results.

*Sampling*

*Sampling*

The sample is a fraction of the population selected for observation and analysis.
By considering the characteristics of the sample, certain conclusions can be
drawn about the characteristics of the population from which the sample comes.
The observed results in the sample can also be derived for possible changes in
the population (using the statistics described in Chapter 11).

Contrary
to popular belief, the samples are not randomly selected. Instead, they are
selected systematically at random to take advantage of odds or probability
operations. If random selection is not possible, other systematic means are
used.

The concept of sampling is discussed in more detail in Chapter 6, which discusses threats to external validity and summarizes. Currently, we are concentrating on sampling.

*Randomness*

*Randomness*

The
concept of chance is very important for scientific observation and research.
This is based on the assumption that although individual events cannot be
predicted accurately, combined events can. For example, although a person's
academic performance may not be predicted accurately, it accurately predicts
a group's average academic performance.

Randomization
has two important applications in research:

- Select
a group of observers who represent the population that the researcher
wants to group, or
- The equation of the test group and the control group in one test. Designating people with a random distribution (everyone in the sample has an equal and independent chance of being assigned to each group) is the best way to ensure their equality.

It
is important to note that random samples are not necessarily identical
representations of the population. The characteristics of consecutive random
samples from the same population may differ to some extent, but it is possible
to assess deviations from population characteristics and from one another.

These
changes, known as experimental failures, do not mean that the sampling process
failed. In contrast, sampling error refers to the random variations that occur
during the sample. With randomization, these variations can be predicted and
accounted for in data analysis techniques.

The topic of sampling error is discussed in more detail in Chapter 11 when the
center boundary theorem, mean standard error, and level of significance are
discussed.

*The Simple Random Sample*

*The Simple Random Sample*

Individual
or individual observations are selected so that everyone has an equal and
independent opportunity to be selected. If the researcher wants to interview 50
people out of 600 students at a school, they can enter 600 names into the
container and draw the names blindfolded until a sample of 50 is selected. This
procedure is complicated and rarely used. Rather, it is a more general table of
random numbers or a computer-generated list.

*Random
Numbers*

*Random Numbers*

An easier way to select a random sample or assign people to trial and control
groups so that they are equated is to use a random number table. Many such tables
are created by computers that produce any sequence of numbers. Rand Corporation
(1965) and Fisher and Yates (1963) statistical tables from the Rand Corporation
(1965) and Fisher and Yates (1963) statistical tables, one million random
numbers with 100,000 normal deviations are widely used in biology,
agriculture, and medical research.

When
tables are used, sequence numbers must be assigned to each member of the
population from which the sample will be selected. By entering a table on any
page, row, or column, the researcher can select a sample from 001 to 999, three
digits and from 0001 to

9999,
four digits. If a duplicate number or a number greater than the population size
is found, it is skipped and the process continues until the desired sample size
is selected.

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