There are 16,007,560,800 or just over 16 billion samples.
Cluster Sampling
at random to represent the population
A random sample should be taken from an entire population.
When the sample - whether it is random or systematic - is somehow representative of the population.
Stratified Random Sampling: obtained by separating the population into mutually exclusive (only belong to one set) sets, or stratas, and then drawing simple random samples (a sample selected in a way that every possible sample with the same number of observation is equally likely to be chosen) from each stratum.
Cluster Sampling
A sample needs to be random and if not a simple random sample of the whole population then a stratified random sample (there are different ways to stratify). Otherwise the study is a waste of time.
at random to represent the population
A random sample is a sample (subset of the population) where each member of the population has an equal chance of being sampled. See related links.
A random sample should be taken from an entire population.
A larger random sample will always give a better estimate of a population parameter than a smaller random sample.
random sample or probability sample
Take a simple random sample.
The sample is a subset of the population. For example, the population may be all the people at your school. A sample might be 5 people from each class. There are different types of sampling methods. The most commonly used is a simple random sample. When your obtain data from the entire population this is called a census.
A biased sample is a sample that is not random. A biased sample will skew the research because the sample does not represent the population.
A biased sample is a sample that is not random. A biased sample will skew the research because the sample does not represent the population.
Information obtained from the sample can be extrapolated to the whole population using statistics.