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What difference between Statistical Sampling and non-statistical sampling?
Statistical sampling is an objective approach using probability to make an inference about the population. The method will determine the sample size and the selection criteria of the sample. The reliability or confidence level of this type of sampling relates to the number of times per 100 the sample will represent the larger population. Non-statistical sampling relies on judgment to determine the sampling method,the sample size,and the selection items in the sample.
What is replicated or interpenetrating sampling?
It involves selection of a certain number of sub-samples rather than one full sample from a population. All the sub-samples should be drawn using the same sampling technique and each is a self-contained and adequate sample of the population. Replicated sampling can be used with any basic sampling technique: simple or stratified, single or multi-stage or single or multiphase sampling. It provides a simple means of calculating the sampling error. It is practical. The replicated samples can throw light on variable non-sampling errors. But disadvantage is that it limits the amount of stratification that can be employed. IPS(interpenetrating sampling) provides a quick, simple, and effective way of estimating the variance of an estimator even in a complex survey. In fact, IPS is the foundation of modern resampling methods like Jackknife, bootstrap, and replication methods. In IPS, three basic principles of experimental designs, namely, randomization, replication, and local control, are used. IPS is used extensively not only in agriculture, but also in social sciences, demography, epidemiology, public health, and many other fields.
What is the mean of the sampling distribution of the sample mean?
Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the "normal distribution." When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.
What is the advantages of random sampling?
Please read related link on what defines a simple random sample. When a sample is done randomly, then every item in the population has an equal chance of being selected. An advantage of random sampling is unbiased statistics. An unbiased statistic has the characteristic that as the sample size increases, the statistics from the sample approaches the true values of the population. This is true if the probability distribution of the population is not changing with time, or as a result of being sampled. Using a random sampling method does not guarantee statistics free of bias. For instance, if I wanted to produce a biased result, I might ask loaded questions. I might also pick particular city, say Chicago, and ask people at random for their favorite team. Obviously, my statistic is not valid outside of Chicago. A second advantage is that the statistical analysis related to sample distributions, hypothesis testing, and sample size determinations assume that the sample is a simple random sample. Remember. the goal of all sampling methods is to obtain information that is representative of the population that is under study. It may not be practical to do a random sample in many cases. For example, suppose I want to know how many people die before age 45 in the world. My random sample would have to include people any country. You can find more information on random sampling and other methods by searching under random sampling methods.
Suppose MyBook has roughly 1000000 users and as each member signs up for an account he or she is assigned a number. Which of these would result in the smallest sample size using systematic sampling?
Surveying every 25,000th number
