2.3 Bias and confounding variables

Bias and confounding variables can seriously skew research results. These pesky factors sneak into studies, causing systematic errors that lead to wrong conclusions. It's crucial for researchers to spot and control them.

Selection bias, measurement bias, and observer bias are common culprits. Confounding variables muddy the waters too. Researchers use control groups, randomization, and blinding to fight back. These tools help ensure solid, trustworthy findings.

Types of Bias

Selection Bias and Measurement Bias

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• Bias occurs when there is a systematic error in the design, conduct, or analysis of a study that results in a mistaken conclusion
• Selection bias happens when the sample is not representative of the population intended to be analyzed
  • Can occur when subjects self-select into a study (volunteers)
  • Certain subgroups may be more motivated to participate (those with strong opinions on the topic)
• Measurement bias refers to systematic errors in data collection that lead to inaccurate or inconsistent measurements
  • Using a scale that consistently underweighs items by 2 pounds introduces measurement bias
  • Poorly worded survey questions can lead respondents to answer inaccurately

Observer Bias

• Observer bias occurs when a researcher's expectations, beliefs, or prejudices influence their observations or interpretation of data
• Researchers may subconsciously look for data confirming their hypotheses while overlooking contradictory data (confirmation bias)
  • A researcher who believes a new drug is effective may focus on improvement in treated patients and downplay side effects
• Observer bias can be introduced by inadequate blinding of researchers
  • If researchers know which treatment a subject received, it may influence their measurements or assessments
• Carefully training observers and using objective, validated measurement tools helps minimize observer bias

Confounding Factors

Confounding Variables

• A confounding variable is an extraneous variable that correlates with both the dependent and independent variables
• Confounding variables provide alternative explanations for the apparent relationship between the independent and dependent variables
  • In a study on the effect of alcohol on heart disease, smoking is a confounding variable because it is associated with alcohol consumption and independently affects heart disease risk
• Confounding can lead to erroneous conclusions about cause and effect relationships
• Researchers must identify potential confounders and control for them through study design or statistical analysis

Placebo and Hawthorne Effects

• The placebo effect is a beneficial effect produced by a placebo drug or treatment that cannot be attributed to the placebo's properties
  • Patients given a sugar pill may report improved symptoms because they believe they are receiving an active medication
• The Hawthorne effect refers to individuals modifying their behavior due to awareness of being observed
  • Factory workers may temporarily increase productivity when they know they are being studied
• Both placebo and Hawthorne effects can confound study results by introducing changes not caused by the independent variable
• Using placebos and blinding subjects to their participation in a study helps control for these effects

Controlling for Bias

Control Groups and Randomization

• A control group is a group of subjects that does not receive the experimental treatment
  • Allows researchers to compare outcomes between treated and untreated groups
  • Helps isolate the effect of the independent variable by holding other factors constant
• Randomly assigning subjects to treatment and control groups ensures the groups are equivalent and minimizes confounding
  • Randomization balances both known and unknown confounding factors between groups
• Using a control group and randomization are key strategies for reducing bias and confounding in experiments

Blinding Techniques

• Blinding refers to concealing information about treatment allocation from subjects, researchers, or both
• In a single-blind study, subjects do not know which treatment they are receiving, but researchers do
  • Prevents subjects' expectations from influencing outcomes (placebo effect)
  • Used when blinding researchers is not feasible (e.g., surgical trials)
• In a double-blind study, neither subjects nor researchers directly involved know who is receiving each treatment
  • Eliminates both subject and researcher bias
  • Considered the gold standard for reducing bias in clinical trials
• Triple-blinding extends blinding to data analysts to prevent bias in analysis and interpretation of results