The stratified sampling technique, also known as stratified random sampling, is a data collection method that breaks a larger population into different strata (subgroups). The number of strata and the sample size of each stratum depends on the total number of respondents in a study. One way to use this probability sampling method is to break Take the population elements corresponding to these numbers as the sample. stratified random sample is a sampling plan in which the population is divided into mutually exclusive and exhaustive strata and a simple random sample of n_h elements is taken within each stratum . The sampling is performed in each strata. Using Random or Simple Sampling, the samples are chosen randomly from a given population of claims. Using Stratified Sampling, the whole population is stratified, or divided into logical groups (i.e., perhaps by dollar paid values), then random sampling is applied within each strata. We’ll spend this webinar discussing the following: Random page 74 Table 3.3 Estimates from an optimally allocated stratified simple random sample (n = 8); the Province’91 population. NOTE: In this data set, the fpc changes with the strata. This is different from all of the previous examples. There are two main takeaways from this article. First, consider conducting stratified random sampling when the signal could be very different between subpopulations. Second, when you use stratified random sampling to conduct an experiment, use an analytical method that can take into account categorical variables. Procedure for sample mean: We calculate the proportions of stratums: wi = Ni/N w i = N i / N. Do simple random sampling for each stratum and calculate stratum sample mean Xi¯ X i ¯. Sample mean is: X¯ = ∑wiXi¯ X ¯ = ∑ w i X i ¯. Equation for population variance inside stratum 1 (symetrical for others): N1−n1 (N1−1)n1 ∑N1 k=1(Xk .

what is stratified random sampling