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
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