Stratified Random Sampling Quiz

What is the basic concept of stratified random sampling?

• Randomly selecting samples from a single stratum without dividing the population

• Choosing samples only from the most populated stratum

• Selecting samples from the entire population without any division

• Dividing the population into subgroups or strata and then randomly selecting samples from each stratum

How do we select samples in stratified random sampling?

• By dividing the population into subgroups based on certain characteristics and then randomly selecting samples from each stratum.

• By choosing samples only from the largest stratum

• By asking individuals to volunteer for the sample

• By selecting samples from the entire population randomly

What are the advantages of using stratified random sampling?

• It ensures that each subgroup is adequately represented in the sample, leading to more precise and reliable results.

• It leads to biased and inaccurate results

• It only focuses on one subgroup and ignores the rest

• It is time-consuming and expensive

What is the difference between stratified random sampling and simple random sampling?

• Stratified random sampling involves selecting individuals from the entire population without any subgroups.

• Stratified random sampling involves dividing the population into subgroups and then taking random samples from each subgroup, while simple random sampling involves selecting individuals from the entire population without any subgroups.

• Simple random sampling involves dividing the population into subgroups and then taking random samples from each subgroup.

• Stratified random sampling involves selecting only the most common individuals from the population.

Can you provide an example of a real-life application of stratified random sampling?

• Weather forecasting

• Space exploration

• Market research

• Agricultural production

What is the main purpose of stratified random sampling?

• To ensure that the sample is not representative of the population

• To make the sampling process more complicated

• To ensure representation of all subgroups within a population

• To only select participants from one specific subgroup

How does stratified random sampling help in reducing sampling error?

• It reduces the sample size, increasing the sampling error.

• It ensures that all subgroups are represented in the sample, reducing the sampling error.

• It only includes the most common subgroups, increasing the sampling error.

• It randomly selects participants without considering subgroups, increasing the sampling error.

What are the key steps in designing a sampling plan using stratified random sampling?

• Selecting samples from each stratum using non-random sampling

• Not dividing the population into homogeneous subgroups

• Identifying the population, dividing the population into homogeneous subgroups, determining the sample size for each stratum, and selecting samples from each stratum using random sampling.

• Choosing the sample size first and then identifying the population

How does stratified random sampling ensure representation of all subgroups in the population?

• By dividing the population into subgroups and randomly selecting samples from each subgroup

• By allowing individuals to self-select into the sample

• By excluding certain subgroups from the sampling process

• By only selecting samples from the largest subgroup

What are the potential challenges of using stratified random sampling?

• Simplified sampling design

• Accurate classification of the population into strata, increased complexity in sampling design, and potential difficulty in implementing the sampling process.

• Decreased accuracy in representing the population

• Easy implementation of the sampling process