True sampling is often unknown because it requires access to the entire population and complete knowledge of its characteristics, which is rarely feasible in practice. Additionally, biases in data collection methods, non-response rates, and the inherent variability within populations can skew results, making it difficult to ascertain an accurate representation. As a result, researchers often rely on probabilistic sampling techniques and statistical inference to estimate population parameters rather than achieving true sampling.
The statement is true that a sampling distribution is a probability distribution for a statistic.
Non probability sampling and probability sampling are different because probability sampling uses random samples. Non probability sampling aren't random, but can still be representative of the population as a whole if done correctly.
normal distribution
The only way to get rid of sampling error is to use the entire population under study. This is usually impossible, so the next best thing is to use large samples and good sampling methods.
normal distribution
the sample mean cannot be computed
The statement is true that a sampling distribution is a probability distribution for a statistic.
Non probability sampling and probability sampling are different because probability sampling uses random samples. Non probability sampling aren't random, but can still be representative of the population as a whole if done correctly.
normal distribution
In probability sampling,every item in the population has a known chance of being selected as a member.In non-probability sampling, the probability that any item in the population will be selected for a sample cannot be determined.
Simple random
Chorionic villus sampling
The only way to get rid of sampling error is to use the entire population under study. This is usually impossible, so the next best thing is to use large samples and good sampling methods.
normal distribution
chorionic villus sampling
Advantages of cluster sampling include that it's inexpensive, fast, and simple. A disadvantage is that it is known to have a high sampling error.
As n increases the sampling distribution of pˆ (p hat) becomes approximately normal.