Selection bias
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Selection bias is a type of bias created when the data sampled is not representative of the data of the population or group that a study or model aims to make a prediction about. Selection bias is the result of systematic errors in data selection and collection. Practically speaking-speaking selection bias often occurs when the sample size of data is incorrect or the assignment of patients or data to groups is nonrandomnon-random. There are many types of selection bias described in the medical literature such as , for example survival bias and survivorship bias which can have overlapping definitions.
Selection bias is of particular concern in terms of the development of artificial intelligence technologies 1. In terms of machine learning, training data to create an algorithm (as opposed to the sampled data in a study) may have a different distribution of pathology than the population for which the algorithm is intended.
-<p><strong>Selection bias</strong> is a type of bias created when the data sampled is not representative of data of the population or group that a study or model aims to make a prediction about. Selection bias is the result of systematic errors in data selection and collection. Practically speaking selection bias often occurs when the sample size of data is incorrect or the assignment of patients or data to groups is nonrandom. There are many types of selection bias described in medical literature such as survival bias and survivorship bias which can have overlapping definitions. </p><p>Selection bias is of particular concern in terms of the development of <a href="/articles/artificial-intelligence">artificial intelligence</a> technologies <sup>1</sup>. In terms of <a href="/articles/machine-learning-overview">machine learning</a>, training data to create an algorithm (as opposed to the sampled data in a study) may have a different distribution of pathology than the population for which the algorithm is intended.</p>- +<p><strong>Selection bias</strong> is a type of bias created when the data sampled is not representative of the data of the population or group that a study or model aims to make a prediction about. Selection bias is the result of <a title="systematic error" href="/articles/systematic-error">systematic errors</a> in data selection and collection. Practically-speaking selection bias often occurs when the sample size of data is incorrect or the assignment of patients or data to groups is non-random. There are many types of selection bias described in the medical literature, for example <a title="Survival bias" href="/articles/survival-bias">survival bias</a> and <a title="Survivorship bias" href="/articles/survivorship-bias">survivorship bias</a> which can have overlapping definitions.</p><p>Selection bias is of particular concern in terms of the development of <a href="/articles/artificial-intelligence">artificial intelligence</a> technologies <sup>1</sup>. In terms of <a href="/articles/machine-learning-1">machine learning</a>, <a title="Training, testing and validation datasets" href="/articles/training-testing-and-validation-datasets">training data</a> to create an algorithm (as opposed to the sampled data in a study) may have a different distribution of pathology than the population for which the algorithm is intended.</p>