🦘 What Is Stratified Random Sampling

^{Suppose that the sample of students described in the previous section was actually selected by using stratified random sampling. In stratified sampling, the study population is divided into nonoverlapping strata, and samples are selected from each stratum independently. The list of students in this junior high school was stratified by grade}

In stratified random sampling, analysts subdivide the population into separate groups known as strata (singular – stratum). Each stratum is composed of elements that have a common characteristic (attribute) that distinguishes them from all the others. The method is most appropriate for large populations that are heterogeneous in nature.

Stratified random sampling is a method of sampling that involves the group of a population into smaller subgroups known as strata. In stratified random sampling, or stratification, which strata are trained founded on members’ shared attributes or characteristics, such as income or educational attainment.

Stratified random sampling is a method of sampling that involves the division of a population into smaller groups known as strata. In stratified random sampling, the strata are formed based on members' shared attributes or characteristics.

Stratified random sampling reduces the number of samples needed by grouping water use quantities likely to be similar. In this case study, for example, large uses by power plants are separated from smaller irrigation uses, removing some of the sampling variance or randomness.

Stratified Sampling. Stratification refers to dividing a population into groups, called strata, such that pairs of population units within the same stratum are deemed more similar ( homogeneous) than pairs from different strata. The strata are mutually exclusive (non-overlapping) and exhaustive of the population.

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ሗбθвեгл фотወ дрα	ዔ խрсቆዦоσэլυ ኛ	Нኩհ оծаሂոд	Ωнтο рኜ з
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what is stratified random sampling