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 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.
Sample Selection Bias
What is ‘Sample Selection Bias’
Sample selection bias is a type of bias caused by choosing non-random data for statistical analysis. The bias exists due to a flaw in the sample selection process, where a subset of the data is systematically excluded due to a particular attribute. The exclusion of the subset can influence the statistical significance of the test, or produce distorted results.
Explaining ‘Sample Selection Bias’
Survivorship bias is a common type of sample selection bias. For example, when back-testing an investment strategy on a large group of stocks, it may be convenient to look for securities that have data for the entire sample period. If we were going to test the strategy against 15 years worth of stock data, we might be inclined to look for stocks that have complete information for the entire 15-year period. However, eliminating a stock that stopped trading, or shortly left the market, would input a bias in our data sample. Since we are only including stocks that lasted the 15-year period, our final results would be flawed, as these performed well enough to survive the market.
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