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Using Systematic Sampling vs Simple Random Sample "Suppose that a home mortgage company has N mortgages numbered serially in the order that they were granted over a period of 20 years. There is a generally increasing trend in the unpaid balances because of the rising cost of housing over the years.
A cluster sampling would randomly pick 6 majors and use all the students in those majors as the sample. A stratified sampling would take 15 students from each major and those 15 x 50 = 750 students would be the sample. Stratified samples are good when differences in the subgroups might skew the results of the study.
It absolutely depends on exactly in what way the sample was nonrandom. As an example, if I want to estimate the mean height of adult men on the Earth, then a truly random sample of 5 men (from all men on Earth) would probably give a more accurate answer than a convenience sample of 1,000 North Korean peasants that I took, just because I happened to be in North Korea at the time.
(For example, if you also want to look at estimates divided in a particular way, you can stratify by that division so that you ensure a certain amount of sample or accuracy within each grouping.) And technically, stratification is a kind of meta-sample design, since after you've stratified you can apply any kind of sample design you like within ...
As you can see, you start to have diminishing returns where it takes many more people to get modest gains in accuracy. This is why many polls you see in the news hover around 1,000 - 1,200 participants. The math does not change if you are sampling a population of 320 million people or 50,000 people. Here is a simple calculator to use. As long ...
Gibbs sampling is a method which takes the weighting factor into account in a simple way, so that the average is exactly right. Gibbs sampling isn't the only way to compute the correct weighted average. There is a whole family of methods which are called Markov chain Monte Carlo (MCMC), of which Gibbs sampling is an example.
Systematic errors are due to errors in measurement. They can be reduced by techniques such as taking multiple measurements, equipment calibration, etc. Random errors are due to the random nature of the thing being measured. They can sometimes be reduced by techniques such as taking multiple measurements.
For example, if you did an analysis and grouped people by height and found that there was a difference in life expectancy based on those groups, that would be systematic variance. Unsystematic variance is the difference between individuals that doesn't depend on such a grouping, and is therefore essentially random (for the purposes of that ...
This Red Lobster example perfectly encapsulates the state of consolidation that late capitalism in the United States has entered. The systematic dismantling of tangible things which took decades to build has reduced America into a capital scrap yard, it makes less than nothing now.
The easiest way of understanding ancestral sampling is through a simple Bayesian network with two nodes, a parent node X and a child node Y. Let's suppose that the two nodes have an associated Bernoulli distribution with two states True and False.