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.
Chat with our AI personalities
It means that every member of the population has the same probability of being included in the sample.
Because, whatever the underlying distribution, as more and more samples are taken from ANY population, the average of those samples will have a standard normal distribution whose mean will be their average. The normal (or Gaussian) distribution is symmetric and so its mean lies at the centre of the probability distribution.
a "T" or a "Z" score. A "T" Score if comparing a sample. A "Z" Score when comparing a population. Remember, a population includes all observation, and a sample includes only a random selection of the population.
It simply means that you have a sample with a smaller variation than the population itself. In the case of random sample, it is possible.
Circular systematic sampling is a random sampling method. An example is random sampling of households. Assume that a random number generator provides the number 49 as a starting point. Starting with the household that is 49 on the target list, every nth household on the list would be sampled until the desired sample size is reached