Statistical Bias Explained

Statistical Biases Explained

Understanding Statistical Biases

Self-Selection Bias

Occurs when individuals choose whether to participate in a study, potentially skewing results.

Example: A voluntary online survey about internet usage attracts mostly tech-savvy respondents.

Exclusion Bias

Systematic omission of certain groups from a study, leading to unrepresentative results.

Example: A health study excludes participants with pre-existing conditions, potentially missing important data.

Attribution Bias

Tendency to explain behaviors based on internal characteristics rather than external factors.

Example: A manager attributes an employee's poor performance to laziness without considering workload or personal issues.

Non-Response Bias

Occurs when non-respondents differ systematically from respondents, affecting data accuracy.

Example: A political survey receives responses mainly from highly engaged voters, skewing results.

Recall Bias

Inaccurate recollection of past events or experiences, affecting the reliability of retrospective data.

Example: Cancer patients may overestimate their past exposure to potential carcinogens compared to healthy individuals.

Observer Bias

The researcher's expectations or preconceptions influence their observations or interpretations.

Example: A researcher unconsciously rates the effectiveness of a new drug more favorably due to personal investment in the project.

Survivorship Bias

Focusing only on subjects that have "survived" a process, ignoring those that did not.

Example: Studying only successful businesses to determine factors for success, overlooking failed ventures.

Sponsorship Bias

Research outcomes are influenced by the interests of the study's financial sponsor.

Example: A tobacco company-funded study downplays the health risks of smoking.

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