Population and Sampling Distributions
So, if the business was interested in how ‘class’ affected consumers’ demand for a food product, it might divide the population up into different class groups, such as working class males, middle class females etc. A random sample could then be chosen from each of these groups by making sure that they were the same proportions of the sample in each category as in population as a whole.Population and Sampling Distributions So if the population had 10 per cent upper class males, so would the sample. Quota Sampling This sampling method involves the population being segmented into a number of groups which share specific characteristics. These may be based on the age and the sex of the population. Interviewers are then given targets for the number of people out of each segment who they must interview. For example, and interviewer may be asked to interview 10 males between the ages of 18 and 25, or 15 females between the ages of 45 and 60. Once the target is reached, no more people from that group are interviewed. The advantage of this sampling method is that it can be cheaper to operate than many of the others. It is also useful where the proportions of the different groups within the population are known. However, results from quota sampling are not statistically representative of the population and are not randomly chosen. They must therefore be treated with caution.
A normal distribution can be regarded as the most important continuous probability distribution in statistics since it can be utilized to model several sets of measurements in business, industry, and nature.Population and Sampling DistributionsFor instance, normal distributions can be used to measure the systolic blood pressure of humans, housing costs, and the lifetime of television sets through random variables. Generally, normal distributions can have any mean and positive standard deviation as the two parameters totally determine the shape of the normal curve during evaluation. In this case, the mean determines the location of the symmetry line while the standard deviation defines how much the data are spread out (“Normal Probability Distributions”, n.d.). In the United States, the age at time of death is more likely to closely approximate a normal distribution than annual income because data on income and income inequality are more controversial. Annual income does not closely approximate a normal distribution because there are different approximation methodologies and the fact that households generally have more than a single individual. Furthermore, annual income does not closely approximate a normal distribution because of the continued growth in income inequality due to various factors such as the high rates of unemployment. Population and Sampling Distributions
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