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Which of these best describes why it is so important to have a representative sample?
To generalize results from the sample population to the target population.
Difference between sample and sampling frame?
A sample is a subset of a population that is selected for research or analysis. It represents a smaller group that is studied to make inferences about the larger population. A sampling frame, on the other hand, is a list of all the elements in the population from which the sample is drawn. It serves as the source from which the sample is selected and should ideally include all members of the population.
What are the disadvantages for random sampling?
its time consuming and expensive if its a large sample you need or a big target population
One weakness of self selected sample?
it is non-random and prone to bias unrepresentative of target population
What is the difference between stratified random sampling and cluster sampling?
Basically in a stratified sampling procedure, the population is first partitioned into disjoint classes (the strata) which together are exhaustive. Thus each population element should be within one and only one stratum. Then a simple random sample is taken from each stratum, the sampling effort may either be a proportional allocation (each simple random sample would contain an amount of variates from a stratum which is proportional to the size of that stratum) or according to optimal allocation, where the target is to have a final sample with the minimum variabilty possible. The main difference between stratified and cluster sampling is that in stratified sampling all the strata need to be sampled. In cluster sampling one proceeds by first selecting a number of clusters at random and then sampling each cluster or conduct a census of each cluster. But usually not all clusters would be included.
