 Principles of Systematics, Module 112, Stratified Sampling. Stratified sampling is basically a type of probability sampling. In this type of sampling, we divide our population. We divide our target population into various groups. We call these groups strata and then we randomly select the strata from the groupings we have made. We divide our population into various groups or strata and then we select our sample from each strata randomly. For example, we want to collect data from different human beings. We want to know which age group's individuals have the habit of smoking. We make three age groups. One age group is between 1 and 20. The other age group is between 21 and 40. And the third age group is above 41. So what we did was we divided our population into different strata and different groups based on some specific criteria. The criteria we have here was age. So based on age, we made three categories. Now, because the number of individuals in each group is very high, it is not possible for us to record the data of all the individuals in each group. For example, we want to study the population of Lahore. So it is not possible for us to record the data of all the individuals in Lahore. So what will we do? First of all, we will divide our population into various groups and strata based on some specific criteria. For example, we divided our population based on age group. Then we randomly collect data of 1000, 1000, 1000 and 1000 from the different parts of Lahore. We collect data of 1000, 1000, 1000 and 1000 from the different parts of Lahore. Similarly, we collect data of 1000 from 21 to 40. How many smokers are there? How many non-smokers are there? Similarly, 41 and above. So this type of sampling is called stratified sampling. For example, if we look at it, we want to know how is the distribution of insects on plants. What do we do? We divide the plant based on height. We say that we made a group of insects at the height of 1 cm to 5 cm. We made another group of insects on the plant of 6 to 10 cm. Similarly, we made a separate group of insects above the height of 11 cm. So what did we do? On the base of height, we divided the plant into different strata, different groups. Now in a field, we cannot record all the plants' data. What do we do? We randomly select 100 plants. And we collect data of 1 to 5 cm from those 100 plants. We collect data of 6 to 10 cm. And we record data of 11 to above. And we find out what types of insects are found on different heights. Similarly, we can divide the population based on weight, IQ or criteria. So in stratified sampling, we divide the population into various groups. And these groups name them strata, then we collect our sample from each strata, from each group randomly. Random selection is done here so that there is no biasness.