 Hello and welcome to the session. In this session we are going to discuss types of samples. Samples chosen can be of two types one is biased sample and the other is unbiased sample. Now we shall discuss what an unbiased sample is. An unbiased sample is a random sample which is chosen without any preference. An unbiased sample is chosen so that it can accurately represent the entire population. There are three ways in which an unbiased sample is chosen. First is simple random sample. Second stratified random sample. And the third is systematic random sample. Now we shall explain each one with the help of an example. So first we have simple random sample. Now in this method of choosing sample any item or person is chosen from the entire population here all have equally likely chances to be included in the sample. For example a teacher chooses any 10 students from the class without any preference is an example of simple random sample. Next we have stratified random sample. In this method the population is divided into similar non-overlapping groups and then a simple random sample is selected from each group. For example the students in a school are the population and they are divided in grade levels. We randomly select two students from each grade level. The selected student from each grade will form a sample. Next we have systematic random sample. In this method items or people are chosen according to a specific time or time interval. For example every 15th customer is chosen for a free gift in a shopping mall. It means after the interval of 15 customers the 15th customer is chosen. Here we have seen the three ways in which an unbiased sample is chosen that is simple random sample, stratified random sample and systematic random sample. Now we shall move on to biased sample. Now in a biased sample one or more parts of the population are favored over the others. There are two ways of choosing a biased sample that is convenient sample and the other is voluntary response sample. Now a convenience sample consists of numbers of the population that are easily accessed. For example to know how many students are attending the school the principal surveys the students in one math class. It means the principal surveyed according to their convenience so a biased sample and the conclusion he draws from this sample may not be accurate. So it cannot lead to a valid conclusion for the population of entire school students. Next we have the voluntary response sample which is another way of choosing a biased sample and it involves only those people who want to participate in the sampling. For example when an online survey is done those who wish to express their opinion completely survey online. From all this we conclude that we can determine the validity of the conclusion if the sample chosen is unbiased and if the sample chosen is biased then conclusion in case drawn are not valid. Let us consider an example a such sandwich store owner wants to make sure that his employees are weighing the meat correctly to put in the sandwiches. He watches the meat being weighed by an employee for 1 hour during the day. Now here we notice that the sample is biased since he is watching only one employee and that too for only 1 hour during the day it cannot draw valid conclusion because there are other employees too who are doing the same job. Let us have one more example. Classify the sample as systematic, simple or stratified. Jason derives his baseball cards by team. Then he randomly selects cards and records the player's IBI that is runs battered in. Now in this example we know that Jason derives his baseball cards by team that is Jason has divided cards in non-overlapping groups and then he randomly selects cards from each group. So it is a stratified random sample. This completes our session. Hope you enjoyed this session.