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In statistics, the term bias is used for describing several different concepts:

• Selection bias, where there is an error in choosing the individuals or groups to take part in a scientific study.
  • A biased sample, sometimes classified as a result of selection bias, is one in which some members of the population are more likely to be included than others.
    • Spectrum bias consists of evaluating the ability of a diagnostic test in a biased group of patients, which leads to an overestimate of the sensitivity and specificity of the test.
• The bias of an estimator is the difference between an estimator's expectation and the true value of the parameter being estimated.
  • Omitted-variable bias is the bias that appears in estimates of parameters in a regression analysis when the assumed specification is incorrect, in that it omits an independent variable that should be in the model.
• In statistical hypothesis testing, a test is said to be unbiased when the probability of rejecting the null hypothesis exceeds the significance level when the alternative is true and is less than or equal to the significance level when the null hypothesis is true.
• Systematic bias or systemic bias are external influences that may affect the accuracy of statistical measurements.
• Data-snooping bias comes from the misuse of data mining techniques.

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