this short animated video explains the concept of sampling and the different types of sampling that
are used in research so don't go anywhere else just sit back relax and enjoy the video hello
and welcome to yet another video series from distributed learning your one-stop solution for
all your learning needs so before we understand sampling and its type let us first understand the
difference between these two terms population and sample the sample is a small group selected from
population to represent the entire population so when you conduct research about a group of people
it is almost impossible to collect data from every person in that group instead you select
a sample and the sample is a group of individuals who will actually participate in the research and
will represent the entire population sample is basically a subset of population that is based
on the fact that it is drawn from the population now what is population operation is a group from
which the sample is drawn exact population will depend upon the scope of the study let
us understand with some real-life examples here millions around the world are infected because
of this covert in nineteen and many companies are in progress of developing vaccines and then in the
process they are also doing some clinical trials so they will select a small portion of people from
different background probably age gender and those who are infected with covered 19 as a sample and
then will perform a study on these individual because it's not possible to conduct tests on
millions of individuals at one time so that is a difference between the population and sample
now it understand what is sampling sampling is a method that allows researchers to infer
information about population based on the results from sample without having to investigate every
individual so reducing the number of individuals in a study reduces your cost You've workload and
may make it easier to obtain a high-quality information to draw a valid conclusion from
your result you have to carefully decide how will you select a sample that is the representative of
the group as a whole so broadly speaking they are two different categories of sampling one is the
probability sampling and next is a nonprobability sampling probability sampling so probability
sampling is based on the fact that every member of population has known an equal chance of being
selected this method is based on the theory of probability for example when you flip a coin
there are a 50/50 chance of getting a head or tail and if you flip a coin once more again the
chance of getting a head or tail is 1550 even if you flip a coin a hundred times the next time when
you flip a coin the chance of getting a head or tail will still be 50 or 50 and in that case you
still want to investigate the coin and why it is coming up head or tail every time the bottom
line of random selection process here is that equal probability and the independence of events
when we talk about the nonprobability sampling it involves random selection based on conveyance and
other criteria allowing you to easily collect initial data so the main focus is that it is
flowing you to select samples based on conveyance probabilities sampling so probability sampling is
based on the fact that every member of population has known an equal chance of being selected there
are four types of probability sampling the first is the simple random sampling so this probe simple
random sampling is a technique in which every member of study population has equal chance
of being selected in the selection of items completely depends on chance and randomness
and therefore this technique is also known as method of chance let us assume that we have this
population which represents a broad category of people of different age sex nationality and
profession and if we have to apply this simple random technique we will select a sample from
this population based on randomness and my chance so the sample that we have are from these four
individuals that we have selected so these are selected based on randomness and by chance the
logic behind using this simple random technique is to remove the bias on the selection process
next is the systematic sampling in systematic sampling the first element is selected randomly
from the list or from the sequential files and then every endeth element is selected let us
assume again that we have this population which again represent the broad category of people and
if we have to apply this technique of systematic sampling we will select sample from this on a
based on the fact that we will select first sample randomly it could be anyone and then
we will apply the innate element from this list so in this scale we will pick the third element and
remember from the population every third member on the sequence will picked from the population will
present the systematic sampling this method is different from simple random sampling since every
possible sample of an earth element is not likely to be equal clusters sampling so cluster sampling
is a sampling procedure that involves randomly selecting a particular cluster of an element from
a group of population and subsequently selecting every element of a selected cluster for study
so with cluster sampling the researchers will divide the population into separate groups called
clusters which could be groups of externally homogeneous but internally heterogeneous groups
so then a simple random sample of a cluster is selected from a population it is assumed
that we have this population which represents a broad category of people of different age sex
nationality and profession and if we have to apply this sampling technique they have been divided the
entire population into different groups which is externally homogenous but internally heterogeneous
group called clusters and after identifying a particular cluster that we will use for using
this thing we will pick all the elements of that selected cluster to be included in the study
stratified sampling so the probability sampling procedure that different walls dividing the
entire population into groups or strata defined by presence of certain critics like your age maybe
based on geography like north south east west or maybe male or female and then randomly selecting
sample from each strata so let us assume that we have against this population which represents a
broad category of people and we divide this entire population into different strata so let us first
divide this into different strata in this case we are dividing waisting based on mailing female
Adele elderly people and the three status now we will select a sample from this different three
status based on selecting one person from each strata at least so this is the stratified sampling
so did you note the difference between the cluster sampling in the States stratified sampling so with
stratified sampling sample includes a ribbon from each stratum but with Crystal sampling
samples include element only from one selected cluster there is a difference between stratified
sampling and the cluster sampling nonprobability sampling so nonprobability sampling individuals
are selected based on non-random criteria and not every individual has a chance of being included
in the study this type of sampling is easier and cheaper to access but you can't use it to
make a valid statistical inference about the whole population there are few types of nonprobability
sampling first is a convenience sampling it involves selecting sample based on convenience
a convenience sample simply includes individuals who happens to be most accessible to the this is
easy and inexpensive way to gather the initial data but there is no way to tell if the sample
is a true representation of entire population so it can't produced in a rice result it is
also known as accidental sampling the next type of nonprobability sampling is the snowball sampling
here you select samples and ask them to refer them to refer you to others it is also called
known as snowball sampling because in theory once you have a ball rolling it picks up more
and more snow along the way and becomes large and larger it is also known as Network sampling
so nonprobability sampling we have another category like quota sampling so quota sampling
means to take very tailored sample that is in proportion to some characteristic
or traits of a population for example you divide a publishin by state they live in
income or education level or may be a male or female this method of sampling is often used
by market researchers where interviewers are given a quota of subject of a specific type to
attempt to recruit for example an interviewer might be told to go to and select 20 adult men
20 adult women 10 teenage girls and then 10 teenage boys so that they could interview them
about their television viewing another type of category is purposive or judgmental sampling it
involves selecting samples based on or his own judgment this technique relies on judgment of
the researchers who choose the sample based on his own experience this approach is often used
by media when canvassing the public for opinions in the qualitative research so that is all I have
on this video see you soon in my next video now you can follow this still elearning on
all the social media platforms like Instagram YouTube Twitter and for a regular updates you
can join our Facebook and Lincoln in groups I will share that link for all these in my
description below thanks for watching distantly learning I hope you liked this video don't forget
to like and share this video with all your friends on all the social media platforms