Suppose that the previous student sample is actually selected by using a stratified sample design. The strata are the grades in the junior high school: 7, 8, and 9. Within the strata, simple random samples are selected. Table 114.1 provides the number of students in each grade.
It is generally observed that random sampling is the best way of doing sampling due to not involving any biased factor. Example. – A polling on a national level via digital platform or as you have seen in certain videos in youtube also that youtubers asking for our suggestions in the form of MCQ in polling button featured in youtube.
Stratified sampling is a probability sampling method in which a researcher divides the population of interest into different subgroups called strata. This type of sampling technique ensures an adequate representation of each subgroup into the sample to be studied. The researcher in this method divides the population into non-overlapping subgroups.
I am confused with the concepts of stratified sampling and oversampling for imbalanced datasets. From what I read in this question here: why-use-stratified-cross-validation-why-does-this-not-damage-variance-related, stratified sampling helps to make sure the sample contains the sample proportion of each class, so that the sample is representative.
In Stratified Random Sampling, the members of the population is divided into strata then random sampling follows. Strata may be age, gender, educational qual
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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.
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what is stratified random sampling
