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How do you solve simple random sampling?
Put up a no trespassing sign. Could you be (a lot) more specific?
What is the problem of random sampling?
With random sampling, you are hoping to get a representative sample of a whole, however statistically you could get a sample that is very different from the whole it was selected from. The larger the sample proportion of the whole, the better your sample will be. For example, a sample of 10 out of 100 is not as good as 20 out of 100. The bigger the sample the closer to the actual whole average you will get.
A sampling error could occur from?
a poorly designed hypothesis
Is cluster sampling is a type of stratified random sampling?
No. Cluster sampling and stratifed random sampling are different, though often confused. (They may, however, be used in conjunction in some sampling designs.) Both are types of random sampling.STRATIFIED sampling involves identifying a variable that will break up your population into separate homogeneous groups (homogeneous in terms of the variable you are interested in). For example, suppose you want to know about the attitudes of kids about their future. Perhaps you have reason to believe this will change with time. If you collected a sample from high schools, you could stratify by grade, giving you 4 relatively homogeneous groups: freshmen, sophomores, juniors, seniors. Then, a common approach is to sample a similar number from each group.Sometimes, though, separating the groups isn't so clear cut. Perhaps you want to stratify based on religion. You can't tell this from looking at a person. So perhaps you collect sample data and apply strata after the fact! This can be useful, but there are some statistical techniques that require equal (or nearly) sample sizes for the strata.CLUSTER sampling involves breaking your population into fairly similarly sized groups called clusters (try googling MSE for an example). But now you want each cluster to contain a heterogeneous mix of individuals. Then, you take a random selection of these clusters and completely enumerate inside of those selected clusters. The problem with cluster sampling is that the cluster has now become your sample unit, instead of individuals which is what you probably hoped. This can be used for counting species, or just for contacting certain populations like apartment dwellers, nursing home residents, etc. The clusters could be apartment buildings in a city. So instead of taking a random sample of apartment dwellers, you would actually randomly select a few of the buildings and talk to everyone inside! Often, this is much more cost efficient. :)
When is non probability sampling appropriate?
In some situations stratified random sampling may be more appropriate. You may have a population which can be divided up into a number of subsets (strata) such that the difference between units in different strata is much greater than the difference between units within each stratum. A probability sample may not have enough units from some of the smaller strata. A stratified random sample will ensure that each stratum is represented proportionally. In other situations, cluster sampling may be more appropriate. Suppose you wish to visit a sample 1% of all schools in the country. If you were to choose the schools by probability sampling they would be all over the country and you would require a huge amount of time and money to visit them all. What you could do, instead, is to divide up the country into 1000 regions. Select 10 of these regions (1%) and then visit every school in the selected regions. Far less running around!
