Unit 5: Sampling
bias: Bias is a term which refers to how far the average statistic lies from the parameter it is estimating, that is, the error which arises when estimating a quantity. Errors from chance will cancel each other out in the long run, those from bias will not. biased sample: In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others. It results in a biased sample, a non-random sample of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected. If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling. convenience sampling: A sample drawn because of its convenience; it is not a probability sample. For example, I might take a sample of opinions in Berkeley (where I live) by just asking my 10 nearest neighbors. That would be a sample of convenience, and would be unlikely to be representative of all of Berkeley. Samples of convenience are not typically representative, and it is not possible to quantify how unrepresentative results based on samples of convenience are likely to be. Convenience samples are to be avoided, and results based on convenience samples are to be viewed with suspicion. descriptive statistics: A set of brief descriptive coefficients that summarizes a given data set, which can either be a representation of the entire population or a sample. The measures used to describe the data set are measures of central tendency and measures of variability or dispersion. inferential statistics: Mathematical methods that employ probability theory for deducing (inferring) the properties of a population from the analysis of the properties of a data sample drawn from it. It is concerned also with the precision and reliability of the inferences it helps to draw.
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