What you're describing is known as "sampling bias." Sampling bias occurs when the individuals or items selected for analysis do not accurately represent the larger population from which they are drawn. This can lead to skewed results and invalid conclusions because certain characteristics of the population may be overrepresented or underrepresented in the sample.
Common types of sampling bias include:
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Selection Bias: When individuals are chosen for the sample in a way that is not random, leading to a non-representative sample. For example, surveying only people who visit a particular website may not reflect the opinions of the entire population.
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Survivorship Bias: Focusing on those who have "survived" a particular process, leading to a distorted understanding. An example would be analyzing successful companies without considering those that failed.
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Exclusion Bias: When certain groups are systematically excluded from the sample, which might lead to results that do not apply broadly.
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Confirmation Bias: Although not strictly a sampling bias, it refers to the tendency to favor information that confirms existing beliefs, which can influence the selection of data or individuals for testing.
To avoid sampling bias, researchers should aim for random sampling methods, ensure diversity in the sample, and use techniques that promote representativeness.
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