A stratified sample is broken up into groups of similar objects/people/whatever we are sampling. Then, we take a simple random sample from each group, or strata. Combined, this is the sample. This is the MOST ACCURATE type of sampling (shout out to Megan Megee for getting this in class!) because we're ensuring that we have a representative from each group, which is not the case in any other type of sample.
Systematic sampling is based on a patter. We choose a number at random (say, 7) and then take every 7th person/object in the population - that makes up our sample.
A clustered sample divides the population up into groups - but unlike the stratified sample, the groups are not made up of like people/objects. The clusters are filled at random. We use a random number generator to select a cluster, then select either everything in the cluster or take random samples from within that cluster.
Judgmental samples are biased because they are based on our opinions. Every other type of sample is not biased because we use some type of random number generating device to get the samples.
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