Bias (statistics)
Bias (statistics) is a systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others[1]. 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[edit | edit source]
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[edit | edit source]
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[edit | edit source]
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[edit | edit source]
References[edit | edit source]
- ↑ "Bias (statistics)". Britannica. Retrieved 2021-09-30.
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