Bias (statistics)

Bias (statistics) is a systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others<ref>

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</ref>. It is a deviation of results or inferences from the truth, or processes leading to such deviation. Bias can lead to inaccurate conclusions about a population or process based on the data from a sample.

Types of Bias

There are several types of bias in statistics, including:

• Selection bias: This occurs when the sample obtained is not representative of the population intended to be analyzed.
• Confirmation bias: This is a tendency to search for, interpret, favor, and recall information in a way that confirms one's preexisting beliefs or hypotheses.
• Publication bias: This occurs when the outcome of an experiment or research study influences the decision whether to publish or otherwise distribute it.
• Survivorship bias: This is the logical error of concentrating on the people or things that made it past some selection process and overlooking those that did not.

Effects of Bias

Bias can significantly affect the validity of a study or survey. It can lead to over- or under-estimations of certain characteristics or results. In medical research, for example, bias can lead to incorrect conclusions about the efficacy of a treatment or the prevalence of a disease.

Controlling Bias

Bias can be controlled or minimized through various methods, including:

• Randomization: This is the practice of assigning subjects to different treatment groups in an unpredictable manner.
• Blinding: This is a practice to prevent bias in research where the people involved are prevented from knowing certain information that might lead to bias.
• Replication: This involves repeating a study using the same methods but different subjects and experimenters.

See Also

• Sampling (statistics)
• Error (statistics)
• Statistical hypothesis testing

References

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