Have you ever tasted piece of chocolate or cookies or any other sweet before buying in bulk or ? Based on the this you decide whether to buy or not. This concept is known as Sampling.
Importance of Sampling:
Sampling is a statistical technique that allows statistician, researchers to infer about a population based on results generated from Sample. And first we have to really understand what we mean when we talk about data. We cannot usually ask the entire population their opinion on a certain matter, or who they would vote for. So we ask a sample of the people and draw conclusions about the population from that sample. Sometime defining Populations is hard job to define and observe in real life. Samples on other hand is easier as it is less time consuming and less costly. We choose samples over population mainly because of time and resources .
Statistician generally gather data from sample and then uses this data to make an inference about the population. It’s important for us to know with what kinds of data we’re dealing with in this present world, so that we can work out how to deal with that data.
Population vs Sample:
If you want to understand something about how the population would be, then it is often impossible to study entire population. That is where we draw sample from population to infer parameters about the population. Before we understand Sampling and its types, let us first understand the difference between these two terms Population and sample because People often get confused between population and sample. It is not feasible to test all the population that is why we test only small true representation of population to determine characteristic of population. Time and cost are the two most important factor that enables us to choose sample over population.
Sample is a small group selected from a population to represent the entire population. Since sample is drawn from the population, so we can say it is a part or a subset of the population.
- Sample is drawn from Population.
Let understand with some real-life examples. Millions around the world are infected because of this of COVID -19. And many companies are doing the clinical trials. So they select small portion of people from different background probably age, gender who are infected with COVID 19 as sample and then will perform a study on these. These samples would represent millions of people who are infected worldwide. Because it is not possible to conduct test on Millions of individuals
Type of Sampling
Broadly speaking we have two types of samplings : Probability sampling and Non-Probability sampling
Probability Sampling :
In this types of sampling technique, statistician must ensure each and every member has equal and fair chance of being selected. In this case selection of samples is by chance without any bias.
Now, Let us see what different types of probability sampling are there :
1. Simple Random Sampling
In simple random sampling, each and every item has equal and fair chance for selecting from the sample. There are many ways we by which we can get data using simple random sampling. One such method is Lottery based.
2. Systematic Sampling
In systematic sampling, you select the first sample randomly and then select every nth samples based on some logical sequence. Say for e.g. selecting every 5th sample after selecting 1st sample randomly. Lets take an another example wherein all employees of the company as per alphabetical order. In group of 100 people , you randomly select 1st number as 5 and apply rule to select every 10th from onwards. So samples will include (5, 15, 25, 35, and so on), and you end up with a sample of 100 people.
3. Stratified Sampling
In stratified sampling, we divide the population into groups also known as Strata. Then within each Strata, you can select samples randomly. We normally use this technique when population has mixed characteristics. You can divide the population based on (Age, Gender, Zone, Income etc.) Say for e.g. company “X” has 100 employees, out of which 60 are male and 40 are female. In this case, you can divide the group in to two strata Male and Female. You can select 10 samples ( 6 Males and 4 Females) to represent the population.
4. Cluster Sampling
In cluster sampling, we divide the group into clusters. Say for e.g. dividing the population and then randomly selecting each and every sample from that particular cluster.
Non Probability Sampling
Non-Probability sampling is opposite of Probability sampling. Here it involves non random or picked based on convenience. In this case selection of samples is not by chance. Now, let us see what different types of non- probability sampling are there :
1. Convenience Sampling
In convenience sampling, we select samples based on convenience. Sometimes we also call it accidental Sampling Say for e.g. we select samples based on availability and their willingness.
2. Snowball Sampling
In snowball sampling, we select samples and ask them to refer them to their known relatives or friends and so on. It is more like Network Sampling. This type of sampling is normally used when it is difficult to get samples for your study. The number of samples you selected first gives you access to other samples like a rolling snowball as it rolls it collect more and more snow on the way. Say for e.g. your study is related to homeless people. Then you meet first homeless person, collect the data and ask him to refer some other homeless person that he is aware of.
3. Quota Sampling
In quota sampling, sample is selected on based on some particular traits or qualities Say for e.g. company wants to find out what age group of people prefer particular brand of mobile. We divide the population based on quota on the basis of age say 10-19 , 20-30, 30-40, 40 above.
4. Judgmental Sampling
In judgmental sampling, we select samples based on his or her judgment or Verdict.
If you want to understand in more details then watch this below video.
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