Stratified random sampling The objective of stratification is to minimize variances within a stratum, resulting in a narrower confidence interval with the same size sample. Two additional advantages of conducting a stratified sample are that they (1) provide an estimate of the mean or total for each stratum and (2) provide better spatial In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation ( stratum) independently. Stratification is the process of dividing members of the $\begingroup$ This answer makes sense. However, I was thinking stratified (random) sampling, splitting the sample into 2 subgroups of size proportional to the population %'s, would not be a good method if there were 3% the number of males as females.
\n \n\n \n \n what is stratified random sampling
In two stage stratified sampling, sampling occurs twice and at two different levels in the hierarchical allocation of population. To understand this better, lets consider the sampling process for AIDIS survey. The first stage units (FSUs) are villages/blocks depending on rural/urban area.
Stratified sampling is when the population is divided into specific groups and then randomly sampled from those groups. Random sampling is a vital part of psychological research. When the population is randomly sampled, it ensures that the study has more validity because there is no researcher bias.
Stratified sampling: Stratified random sampling is a method of sampling that involves the division of a population into smaller groups known as strata. Samples are then pulled from these strata, and analysis is performed to make inferences about the greater population of interest. An example will help understand stratified sampling better
Pahn.
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    • what is stratified random sampling