Experimental bias
53 flashcards covering Experimental bias for the MCAT Chem / Phys / Psych / Soc section.
Experimental bias occurs when systematic errors in research design, execution, or interpretation lead to skewed results, often influenced by the experimenter's expectations or flaws in the process. For example, if a study favors certain participants or measures outcomes inconsistently, it can produce unreliable data and mislead conclusions. This concept is crucial because it highlights how unintended influences can compromise the integrity of scientific findings, affecting fields from psychology to physics.
On the MCAT, experimental bias frequently appears in psychology, sociology, and sometimes chemistry or physics sections, often in passage-based questions that require identifying biases like selection or confirmation errors. You'll need to spot common traps, such as confusing bias with random variation or overlooking subtle design flaws, and explain how they invalidate results. Focus on recognizing bias types and strategies for minimization, as these skills are key to answering questions about data reliability and experimental validity.
A concrete tip: Practice analyzing real studies to quickly spot potential biases.
Terms (53)
- 01
Experimental bias
A systematic deviation in research results caused by flaws in study design, execution, or analysis that skew outcomes away from the true effect.
- 02
Selection bias
A type of experimental bias where the way participants are chosen affects the results, such as including only certain groups that do not represent the population.
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Sampling bias
Bias occurring when the sample drawn from a population is not representative, leading to inaccurate generalizations about the larger group.
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Confirmation bias
The tendency for researchers or participants to favor information that confirms preexisting beliefs, potentially distorting experimental outcomes.
- 05
Observer bias
Bias introduced when the person collecting data influences results through their expectations or interpretations, often unconsciously.
- 06
Experimenter bias
When the researcher unintentionally influences the study outcomes through their behavior, expectations, or interactions with participants.
- 07
Response bias
Bias in how participants answer questions, often due to wording, format, or social pressures, leading to inaccurate data.
- 08
Publication bias
The tendency for studies with positive or significant results to be published more often than those with negative or null results, skewing the literature.
- 09
Recall bias
A bias where participants inaccurately remember past events, often influenced by current knowledge or emotions, affecting retrospective studies.
- 10
Measurement bias
Error in how variables are measured, such as using imprecise tools or methods, which systematically distorts data.
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Double-blind study
A research design that minimizes bias by keeping both participants and researchers unaware of who receives the treatment versus the control.
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Single-blind study
A study where participants are unaware of their group assignment, reducing bias from their expectations, though researchers know.
- 13
Placebo effect
A phenomenon where participants experience changes due to their belief in treatment, not the treatment itself, introducing bias in efficacy studies.
- 14
Nocebo effect
The opposite of the placebo effect, where negative expectations lead to adverse outcomes, biasing results in clinical trials.
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Hawthorne effect
Bias where participants alter their behavior because they know they are being observed, affecting the validity of study results.
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Demand characteristics
Cues in an experiment that lead participants to guess the hypothesis and change their behavior accordingly, introducing bias.
- 17
Social desirability bias
The inclination of participants to answer in ways that make them appear more favorable, skewing self-report data.
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Anchoring bias
A cognitive bias where initial information overly influences judgments, potentially affecting how data is interpreted in experiments.
- 19
Systematic error
A consistent, repeatable error in measurements that biases results in one direction, unlike random error.
- 20
Random error
Unpredictable variations in measurements that do not systematically bias results but can increase uncertainty.
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Interviewer bias
Bias introduced when the interviewer influences responses through their tone, appearance, or questions, affecting data quality.
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Reporting bias
The selective reporting of outcomes in studies, where only favorable results are highlighted, distorting the overall evidence.
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Confounding variables
Factors that correlate with both the independent and dependent variables, potentially causing bias by masking the true relationship.
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Internal validity
The extent to which a study accurately establishes a cause-effect relationship, threatened by biases like confounding variables.
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External validity
The ability to generalize study findings to other settings, which can be compromised by sampling bias.
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Maturation effect
A threat to internal validity where changes in participants over time, not the treatment, cause outcomes, introducing bias.
- 27
Testing effect
Bias from repeated testing where participants improve due to practice, not the intervention, affecting result interpretation.
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Instrumentation bias
Changes in measurement tools or procedures over time that systematically alter data, leading to inaccurate comparisons.
- 29
History effect
External events occurring during a study that influence outcomes, creating bias in longitudinal research.
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Regression to the mean
A statistical phenomenon where extreme scores tend to move toward the average on retesting, often mistaken for treatment effects.
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Attrition bias
Bias from participants dropping out of a study unevenly between groups, skewing results and reducing generalizability.
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Carryover effect
In repeated measures designs, the influence of one treatment on subsequent ones, biasing results if not controlled.
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Order effects
Bias in experiments where the sequence of treatments affects outcomes, such as fatigue from earlier tasks.
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Ceiling effect
When a measure is too easy, most scores are at the maximum, obscuring differences and introducing bias.
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Floor effect
When a measure is too difficult, most scores are at the minimum, hiding variations and biasing results.
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Halo effect
A bias where one positive trait influences perceptions of other traits, affecting subjective measurements in studies.
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Horn effect
The opposite of the halo effect, where one negative trait colors judgments of other attributes, introducing bias.
- 38
Leniency bias
A tendency in rating systems to give higher scores than deserved, systematically skewing evaluative data.
- 39
Central tendency bias
The avoidance of extreme ratings in assessments, leading to clustered data around the middle and reduced accuracy.
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Contrast effect
Bias where judgments are influenced by comparisons to previous stimuli, altering perceptions in sequential evaluations.
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Primacy effect
The tendency to weigh initial information more heavily than later information, potentially biasing overall assessments.
- 42
Recency effect
The inclination to remember and emphasize recent information over earlier details, affecting data recall in studies.
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In-group bias
Favoritism toward one's own group in research settings, which can skew interpretations of intergroup behaviors.
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Out-group bias
Negative perceptions of groups outside one's own, introducing bias in studies involving social dynamics.
- 45
False consensus effect
The bias where individuals overestimate how much others share their beliefs, affecting survey-based research.
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Bandwagon effect
A bias where people adopt beliefs or behaviors because others do, potentially influencing experimental group dynamics.
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Parallax error
An observational bias in physics experiments where the angle of viewing misaligns measurements, like in a ruler reading.
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Zero error
A systematic bias in instruments where the zero point is offset, causing all measurements to be inaccurate by a constant amount.
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Calibration bias
Error from improperly calibrated equipment, leading to consistent over- or under-estimation in scientific measurements.
- 50
Instrumental bias
Bias due to flaws in experimental tools or devices that systematically affect data collection in physical sciences.
- 51
Pygmalion effect
A bias where higher expectations from researchers lead to better performance in participants, as in educational studies.
- 52
Attribution bias
The tendency to attribute causes to personal factors rather than situations, affecting interpretations in psychological experiments.
- 53
Availability heuristic
A mental shortcut relying on immediate examples, which can bias judgments in decision-making studies.