what is repeated sampling?
It could mean almost anything, but I suspect I know what you are asking. See this original post by Professor Wang, and then the response to it (asking what is repeated sampling), and his answer to it.
Again, your use could mean other things.
http://stats.stackexchange.com/questions/22561/difference-between-likelihood-principle-and-repeated-sampling-principle
Repeated sampling refers to the process of conducting multiple samples from a population in order to make statistical inferences about the population. It involves drawing multiple samples from the same population and analyzing each sample separately to observe patterns and estimate population parameters.
The purpose of repeated sampling is to reduce the impact of sampling variability and increase the accuracy and precision of the statistical estimates made from the samples. By taking multiple samples, we can better understand the range of possible values for population parameters and improve the reliability of our conclusions.
Repeated sampling is commonly used in statistical analysis, especially in hypothesis testing and estimation. It helps researchers account for random fluctuations and measure the stability of their results across different samples. It also enables them to assess the effect of sample size on the accuracy of estimates and the reliability of statistical tests.
Repeated sampling refers to the process of drawing multiple samples from a population in order to gather information and make inferences about that population. It is a fundamental concept in statistics and is often used to estimate population parameters and assess the variability of the obtained results.
To perform repeated sampling, you generally follow these steps:
1. Define the population: Start by clearly defining the population of interest. This could be any group of individuals, objects, or events that you wish to study.
2. Select a suitable sampling method: Identify an appropriate sampling technique to draw a sample from the population. Common methods include simple random sampling, stratified sampling, and cluster sampling.
3. Draw a sample: Use the selected sampling technique to obtain a sample from the population. The sample should be representative and unbiased, meaning it should adequately reflect the characteristics of the entire population.
4. Analyze the sample: Once you have the sample, analyze the data collected from it. This could involve calculating descriptive statistics, estimating population parameters, or conducting hypothesis tests.
5. Repeat the process: To conduct repeated sampling, repeat steps 3 and 4 multiple times. This involves drawing additional samples from the same population and analyzing each sample separately.
By conducting repeated sampling, you can observe the variations in the results obtained from different samples and assess the precision and accuracy of your statistical estimates. It enables you to account for sampling variability and obtain a more robust understanding of the population characteristics.