This post is in follow-up to the earlier post of Sampling with SAS in November 2016. Let us try to relook and understand the terms.
Profile data of 5000 random students captured to get a feeling of how Technology research program is done in India. These 5000 students are selected from over half a million students enrolled in last 10 years.In this scenario half a million students are called population and random representative 5000 students are called Samples. In the same example say out of 5000 students around 250 random students are interviewed to understand various geo-personal data then for that study 250 is Sample data and 5000 students is population data.
Let us try to further understand this through some formal definitions.
A population is the complete set of observations or the entire group of objects that you are researching. A sample is a subset of the population. You gather a sample so that you don't have to obtain data for the entire population. The sample should be representative of the population, meaning that the sample's characteristics are similar to the population's characteristics. One way to obtain a representative sample is to collect a simple random sample. With this sampling method, every possible sample of a given size in the population has an equal chance of being selected. Random sampling can help to ensure that the sample is representative of the population. You should avoid collecting your sample from a section of the population that is easily available to you. This is called convenience sampling, and it can lead to a biased sample that is not representative of the population from which it is drawn. A sample that's not representative can cause you to draw incorrect conclusions. Let's look at an example.
Suppose a university wants to estimate the percent of its freshmen who plan to return for their sophomore year. The population for this study is the entire set of 2,500 freshmen in attendance. Researchers gathered a representative sample of 100 freshmen by selecting 100 student ID numbers at random from the entire set of 2,500 freshmen. If the researchers had simply selected the first 100 freshmen who responded to an e-mail questionnaire, this would have resulted in a biased sample. This could lead to an incorrect estimate of the number who plan to return for their sophomore year. If you have a representative sample, you can make correct inferences to the entire population. In this course, we always assume that the sample is representative. Click the Information button for information on how to generate random samples.
Profile data of 5000 random students captured to get a feeling of how Technology research program is done in India. These 5000 students are selected from over half a million students enrolled in last 10 years.In this scenario half a million students are called population and random representative 5000 students are called Samples. In the same example say out of 5000 students around 250 random students are interviewed to understand various geo-personal data then for that study 250 is Sample data and 5000 students is population data.
Let us try to further understand this through some formal definitions.
A population is the complete set of observations or the entire group of objects that you are researching. A sample is a subset of the population. You gather a sample so that you don't have to obtain data for the entire population. The sample should be representative of the population, meaning that the sample's characteristics are similar to the population's characteristics. One way to obtain a representative sample is to collect a simple random sample. With this sampling method, every possible sample of a given size in the population has an equal chance of being selected. Random sampling can help to ensure that the sample is representative of the population. You should avoid collecting your sample from a section of the population that is easily available to you. This is called convenience sampling, and it can lead to a biased sample that is not representative of the population from which it is drawn. A sample that's not representative can cause you to draw incorrect conclusions. Let's look at an example.
Suppose a university wants to estimate the percent of its freshmen who plan to return for their sophomore year. The population for this study is the entire set of 2,500 freshmen in attendance. Researchers gathered a representative sample of 100 freshmen by selecting 100 student ID numbers at random from the entire set of 2,500 freshmen. If the researchers had simply selected the first 100 freshmen who responded to an e-mail questionnaire, this would have resulted in a biased sample. This could lead to an incorrect estimate of the number who plan to return for their sophomore year. If you have a representative sample, you can make correct inferences to the entire population. In this course, we always assume that the sample is representative. Click the Information button for information on how to generate random samples.
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