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Population, Sample, and SamplingStats 2020. 1. 11. 14:59
1. Overview
How to select good random samples from the population
How to calculate the estimate of a population mean using samples
How can I assure that the sample will provide adequate information about the population parameters
2. Description
2.1 Simple Random Sampling Method
2.1.1 finite population
A simple random sample of size n from a finite population of size N is a sample selected such that:
- Every observation has an equal chance of being selected
- Every sample of n observation has an equal chance of being selected
2.1.2 infinite population
Select a random sample such that each element is selected independently: Avoid selection bias
2.2 Point estimators
To estimate population parameters we compute sample parameters
2.2.1 Standard Deviation
If the sample size is less than or equal to 5% of the population size :
If the sample size is greater than 5% of the population size >0.05:
where is finite population correction factor
2.3 Sampling Error
The standard error is a measure of the sampling error of the sampling distribution. There are others, but standard error is, by far, the most commonly used when dealing with survey data.
you can see other formulas within this link
2.4 Central Limit Theorem
The sampling distribution of the mean of a random sample drawn from any population is approximately normal for a sufficiently large sample size. The larger the sample size, the more closely the sampling distribution of will resemble a normal distribution.
2.5 Degree of freedom
3. Reference
https://www.youtube.com/watch?v=92s7IVS6A34
https://statisticsbyjim.com/hypothesis-testing/degrees-freedom-statistics/
https://www.investopedia.com/terms/t/tdistribution.asp
https://corporatefinanceinstitute.com/resources/knowledge/other/kurtosis/
https://en.wikipedia.org/wiki/Central_limit_theorem
https://www.en-net.org/question/768.aspx
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