Number of samples = 42C4 = 42*41*40*39/24 = 111930
There are 111,930 such samples.
111930
111930
There are 16,007,560,800 or just over 16 billion samples.
You wouldn't want to use the same item twice, so just divide 38/5 = 7+ ... you can get 7 samples of size 5.
It is called one-stage cluster sampling. If random samples are taken within the selected clusters then it is two-stage cluster sampling.
There are 324,632 possible samples.
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.
There are 16,007,560,800 or just over 16 billion samples.
stratified sampling, in which the population is divided into classes, and random samples are taken from each class;cluster sampling, in which a unit of the sample is a group such as a household; andsystematic sampling, which refers to samples chosen by any system other than random selection.
A 'random' sample - covers all age groups, genders, and other criteria. A 'targeted' sample might only cover a small part of the population.
You wouldn't want to use the same item twice, so just divide 38/5 = 7+ ... you can get 7 samples of size 5.
Data from random samples will not always include the same values. Values are chosen randomly and they may or may not be the same. So means will vary among random samples.
It is called one-stage cluster sampling. If random samples are taken within the selected clusters then it is two-stage cluster sampling.
There are 324,632 possible samples.
Non-probability or Judgement Samples has to do with a basic researcher assumptions about the nature of the population, the researcher assumes that any sample would be representative to the population,the results of this type of samples can not be generalized to the population(cause it may not be representative as the research assumed) and the results may be biased. Probability or Random samples is a sample that to be drawn from the population such that each element in the population has a chance to be in the selected sample the results of the random samples can be used in Statistical inference purposes
Random samples
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.
There are 25C7 different samples of seven from a pool of 25.25C7 = 25!/(7!(25-7)!) = 480 700 different samples of 7
There are two equivalent ways of defining a simple random sample from a larger population. One definition is that every member of the population has the same probability of being included in the sample. The second is that, if you generate all possible samples of the given size from the population, then each such sample has the same probability of being selected for use.