Bias refers to errors or changes in how data is collected, analyzed, interpreted or reviewed that result in incorrect conclusions. Bias can occur at any phase of a study, from its initial design to the final analysis and reporting of results. Identifying and preventing bias is important because it affects the validity and reliability of research findings.
- Selection Bias: Occurs when the participants chosen for the study do not represent the broader population, affecting the generalizability of the findings.
- Information Bias: Happens when there are errors in how data is collected, recorded, or analyzed, leading to inaccurate conclusions.
- Publication Bias: Arises when the outcome of research influences whether it gets published, with positive results more likely to be reported.
- Confounding Bias: Occurs when an outside variable affects both the variable of interest and the outcome, distorting the true relationship.
- Reporting Bias: Involves selectively disclosing positive or favorable results over negative or unfavorable ones in research findings.
- Attrition Bias: Happens when participants drop out of a study in a way that is not random, potentially skewing the results.
- Sponsorship Bias: Results when the funding source of the research influences the study’s outcomes or conclusions, often seen when a study supports the interests of the financial sponsor.
- Response Bias: Occurs when respondents in surveys or studies adjust their answers based on what they believe is the expected response or due to misunderstandings of the question.
- Recall Bias: Arises when participants do not remember previous events accurately, leading to errors in self-reported data.
- Observer Bias: Happens when the researcher’s expectations or prior beliefs influence their observations or interpretations of data.
- Measurement Bias: Occurs when the tools or methods used to collect data are flawed, leading to inaccurate measurements.