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When a population is too large, then a sample of the population would be taken to determine the information wanted. For example if you wanted to find out the average IQ of Americans you would not want to give every person living in the United States an IQ test, because that would be highly unrealistic, instead you would want to sample a random group of Americans from multiple backgrounds to get an average IQ of Americans.

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Q: Give the reasons why sample are prefered to entire population?
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Population or sample psychology?

Portion of the entire population used to estimate what is likely happening within a population.


Difference between sample regression and population regression line?

AnswerA sample is a subset of a population. Usually it is impossible to test an entire population so tests are done on a sample of that population. These samples can be selected so that they are representative of the population in which cases the sample will have weights, strata, and clusters. But usually people use random samples. So it's not that the line is different, it's that the line comes from different data. In stats we have formulas that allow a sample to represent a population, if you have the entire population (again unlikely), you wouldn't need to use this sample formulas, only the population formulas.


What would it mean If the sampling error was zero?

The sample consisted of the entire population.


What is sample and population relationship?

The sample is a subset of the population. For example, the population may be all the people at your school. A sample might be 5 people from each class. There are different types of sampling methods. The most commonly used is a simple random sample. When your obtain data from the entire population this is called a census.


What is the similarities between population and sample?

Similarity: Both are counts of people/animals/things. Difference: Population is the total # of things, while sample is the # of things that you gather data on. If you pick the right sample size, you can be pretty confident that the results of the sample data is the same as the results of the entire population.