For computer simulation, see pseudo-random number sampling. A visual representation of the sampling ansi asq z1 9 2008 pdf. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly stratified sampling.

Successful statistical practice is based on focused problem definition. In sampling, this includes defining the population from which our sample is drawn. A population can be defined as including all people or items with the characteristic one wishes to understand. Sometimes what defines a population is obvious.

For example, a manufacturer needs to decide whether a batch of material from production is of high enough quality to be released to the customer, or should be sentenced for scrap or rework due to poor quality. In this case, the batch is the population. Although the population of interest often consists of physical objects, sometimes we need to sample over time, space, or some combination of these dimensions. For instance, an investigation of supermarket staffing could examine checkout line length at various times, or a study on endangered penguins might aim to understand their usage of various hunting grounds over time. For the time dimension, the focus may be on periods or discrete occasions. In other cases, our ‘population’ may be even less tangible. For example, Joseph Jagger studied the behaviour of roulette wheels at a casino in Monte Carlo, and used this to identify a biased wheel.

This situation often arises when we seek knowledge about the cause system of which the observed population is an outcome. In such cases, sampling theory may treat the observed population as a sample from a larger ‘superpopulation’. Note also that the population from which the sample is drawn may not be the same as the population about which we actually want information. Often there is large but not complete overlap between these two groups due to frame issues etc. 2008 in order to make predictions about people born in 2009. Time spent in making the sampled population and population of concern precise is often well spent, because it raises many issues, ambiguities and questions that would otherwise have been overlooked at this stage. However, in the more general case this is not usually possible or practical.

There is no way to identify all rats in the set of all rats. As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample. The combination of these traits makes it possible to produce unbiased estimates of population totals, by weighting sampled units according to their probability of selection. Example: We want to estimate the total income of adults living in a given street. We visit each household in that street, identify all adults living there, and randomly select one adult from each household. People living on their own are certain to be selected, so we simply add their income to our estimate of the total.

But a person living in a household of two adults has only a one-in-two chance of selection. To reflect this, when we come to such a household, we would count the selected person’s income twice towards the total. The person who is selected from that household can be loosely viewed as also representing the person who isn’t selected. Such designs are also referred to as ‘self-weighting’ because all sampled units are given the same weight. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Example: We visit every household in a given street, and interview the first person to answer the door.

Nonprobability sampling methods include convenience sampling, quota sampling and purposive sampling. Within any of the types of frames identified above, a variety of sampling methods can be employed, individually or in combination. Each element of the frame thus has an equal probability of selection: the frame is not subdivided or partitioned. This minimizes bias and simplifies analysis of results. SRS can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn’t reflect the makeup of the population. For instance, a simple random sample of ten people from a given country will on average produce five men and five women, but any given trial is likely to overrepresent one sex and underrepresent the other.

Postulate the effect size of interest, mentioned several times in the Bible. It is easy to implement and the stratification induced can make it efficient – just as in stratified sampling. Fitting logistic models under case — note also that the population from which the sample is drawn may not be the same as the population about which we actually want information. And potentially reducing the utility of the strata.

SRS may also be cumbersome and tedious when sampling from an unusually large target population. In some cases, investigators are interested in “research questions specific” to subgroups of the population. For example, researchers might be interested in examining whether cognitive ability as a predictor of job performance is equally applicable across racial groups. SRS cannot accommodate the needs of researchers in this situation because it does not provide subsamples of the population. Systematic sampling involves a random start and then proceeds with the selection of every kth element from then onwards. As long as the starting point is randomized, systematic sampling is a type of probability sampling. It is easy to implement and the stratification induced can make it efficient, if the variable by which the list is ordered is correlated with the variable of interest.

However, systematic sampling is especially vulnerable to periodicities in the list. If periodicity is present and the period is a multiple or factor of the interval used, the sample is especially likely to be unrepresentative of the overall population, making the scheme less accurate than simple random sampling. Another drawback of systematic sampling is that even in scenarios where it is more accurate than SRS, its theoretical properties make it difficult to quantify that accuracy. When the population embraces a number of distinct categories, the frame can be organized by these categories into separate “strata.