Generalizing to a population. Sometimes when scientists talk about generalizability, they are applying results from a study sample to the larger population from which the sample was selected. For instance, consider the question, “What percentage of the Canadian population supports the Liberal party?” In this case, it would be important for researchers to survey people who represent the population at large. Therefore they must ensure that the survey respondents include relevant groups from the larger population in the correct proportions. Examples of relevant groups could be based on race, gender or age group.
Generalizing to a theory. More broadly, the concept of generalizability deals with moving from observations to scientific theories or hypotheses. This type of generalization amounts to taking time- and place-specific observations to create a universal hypothesis or theory. For instance, in the 1940s and 1950s, British researchers Richard Doll and Bradford Hill found that 647 out of 649 lung cancer patients in London hospitals were smokers. This led to many more research studies, with increasing sample sizes, with differing groups of people, with differing amounts of smoking and so on. When the results were found to be consistent across person, time and place, the observations were generalized into a theory: “cigarette smoking causes lung cancer.”
Sampling theory is the field of statistics that is involved. with the collection, analysis and interpretation of data gathered. from random samples of a population under study.
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Simple random sampling is the basic sampling technique where we select a group of subjects (a sample) for study from a larger group (a population). Each individual is chosen entirely by chance and each member of the population has an equal chance of being included in the sample. If you test one high school within your community on the North side region and test the senior class male versus female students on smoking cigarettes throughout the school year.
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