A visual representation of the sampling process. Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population. In business and medical simple random sampling examples pdf, sampling is widely used for gathering information about a population. Successful statistical practice is based on focused problem definition.
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. 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. In such cases, sampling theory may treat the observed population as a sample from a larger ‘superpopulation’. For example, a researcher might study the success rate of a new ‘quit smoking’ program on a test group of 100 patients, in order to predict the effects of the program if it were made available nationwide. 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. These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory.
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. We then interview the selected person and find their income.
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. 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.
Hence, because the selection of elements is nonrandom, nonprobability sampling does not allow the estimation of sampling errors. Information about the relationship between sample and population is limited, making it difficult to extrapolate from the sample to the population. Example: We visit every household in a given street, and interview the first person to answer the door. Within any of the types of frames identified above, a variety of sampling methods can be employed, individually or in combination. This minimizes bias and simplifies analysis of results. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy 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. Systematic and stratified techniques attempt to overcome this problem by “using information about the population” to choose a more “representative” sample. 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. Stratified sampling” addresses this weakness of SRS.
10th street number along the street ensures that the sample is spread evenly along the length of the street, representing all of these districts. 10, this bias is eliminated. However, systematic sampling is especially vulnerable to periodicities in the list. When the population embraces a number of distinct categories, the frame can be organized by these categories into separate “strata.