# Stratified Sampling Example Situation

**Stratified Sampling Example Situation**. A research team has decided to perform a study to analyze the grade point averages or gpas for the 21 million college students in the u.s. Using stratified sampling will allow you to obtain more precise (with lower variance) statistical estimates of whatever you are trying to measure.

Defining the population and strata. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size. Conducting a research on online gaming addiction among university students.

### Final Members For Research Are Randomly Chosen From The Various Strata Which Leads To Cost Reduction And Improved Response Efficiency.

The groups that the population is divided into must be. Next, collect a list of every member of the population, and assign each. Separate the population into strata.

### Example Of Stratified Random Sampling.

Stratification may increase efficiency of the estimates by forming strata in such a way that each stratum becomes homogeneous. Includes the diversity of the sample. Stratified sampling is a random sampling method of dividing the population into various subgroups or strata and drawing a random sample from each.

### The Following Steps Can Be Used As A Guideline For Constructing A Stratified Sample.

Some of the benefits of using stratified sampling under such conditions are: A stratified random sample is a population sample that requires the population to be divided into smaller groups, called ' strata '. Stratified sampling helps you to save cost and time because you'd be working with a small and precise sample.

### These Shared Characteristics Can Include Gender, Age, Sex, Race, Education Level, Or Income.

By randomly selecting the start between #1 and #10, this bias. ${n_i}$ = the sample size of i strata. Random samples can be taken from each stratum, or.

### Let’s Move On To Our Next Approach I.e.

Note that if the starting point is house #1, the last number will be #991, thus the sample will be slightly biased to the poor end; The optimal disproportionate sampling should be done in a manner that Example of disproportionate stratified sampling in action.