Frequently Asked Questions

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What is the difference b/w observer bias & actor-observer bias?

Observer bias refers to the tendency of an observer to see what they expect or want to see, which can distort their perception of the situation or behaviour they are observing.

This can result in accurate or complete data, leading to biased conclusions or recommendations. On the other hand, actor-observer bias refers to individuals’ tendency to attribute their behaviour to external situational factors while attributing the behaviour of others to internal or dispositional factors.

This bias can arise due to differences in perspective and information availability between actors and observers, resulting in different perceptions and judgments of behaviour.

Frequently Asked Questions : Research Bias

Observer bias: Tendency to attribute others’ behavior to internal factors and overlook situational influences.

Actor-observer bias: Attributing own actions to external factors, not considering personal traits.

Response bias is when participants answer inaccurately or dishonestly. No-response bias is when participants skip questions, leading to missing data.

Bias in research can be a problem because it can lead to inaccurate or unreliable study results. When bias is present in a study, it can distort the findings, leading to incorrect conclusions or recommendations.

This can have significant consequences, particularly regarding decision-making in healthcare, public policy, and scientific research.
Bias can arise at various stages of the research process, such as during the study design, data collection, analysis, or interpretation.

Some common types of bias in research include selection bias, measurement bias, reporting bias, publication bias, and confounding bias.
Bias can also result from various factors, such as the researcher’s personal biases, unconscious biases, financial interests, pressure to produce positive results or lack of awareness of potential sources of bias. The presence of bias in research can undermine the credibility and validity of study results, reducing the confidence that can be placed in the findings.

This can significantly affect public health, policy decisions, and clinical practice. For example, a biased study that recommends an ineffective or harmful treatment could put patients at risk, waste resources, and have long-term consequences.
Therefore, researchers need to be aware of the potential sources of bias, take steps to minimise them, and report on any limitations or potential biases in their study results.

This can help ensure that the research findings are reliable and accurate and can inform decision-making in a meaningful and impactful way.