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# Stratified randomization can accurately reflect the outcomes of the general population since influential factors are applied to stratify the entire samples and balance the samples' vital characteristics among treatment groups. For instance, applying stratified randomization to make a sample of 100 from the population can guarantee the balance of males and females in each treatment group, while using simple randomization might result in only 20 males in one group and 80 males in another group.<ref name=":0" />
# Stratified randomization can accurately reflect the outcomes of the general population since influential factors are applied to stratify the entire samples and balance the samples' vital characteristics among treatment groups. For instance, applying stratified randomization to make a sample of 100 from the population can guarantee the balance of males and females in each treatment group, while using simple randomization might result in only 20 males in one group and 80 males in another group.<ref name=":0" />
# Stratified randomization makes a smaller error than other sampling methods such as [[cluster sampling]], simple random sampling, and [[systematic sampling]] or [http://dissertation.laerd.com/non-probability-sampling.php non-probability methods] since measurements within strata could be made to have a lower [[standard deviation]]. Randomizing divided strata are more manageable and cheaper in some cases than simply randomizing general samples.<ref name=":1" />
# Stratified randomization makes a smaller error than other sampling methods such as [[cluster sampling]], simple random sampling, and [[systematic sampling]] or [http://dissertation.laerd.com/non-probability-sampling.php non-probability methods] since measurements within strata could be made to have a lower [[standard deviation]]. Randomizing divided strata are more manageable and cheaper in some cases than simply randomizing general samples.<ref name=":1" />
==缺点 Disadvantage ==
==缺点 Disadvantage ==