Bias vs imprecision

Systematic reviews play a crucial role in evidence-based practice (EBP) by synthesising data from multiple studies.

However, the accuracy of these reviews depends not only on precision but also on the quality of the data. This session explores bias and imprecision, two key factors that influence the validity of systematic reviews.

Bias: A Systematic Error

• Definition: Bias is a systematic error that distorts study results, leading to incorrect conclusions.

• Causes: Methodological flaws, selection bias, measurement bias, and publication bias.

• Impact: Results are consistently skewed in one direction, making them unreliable.

Example: Imagine a dartboard where all the darts consistently land away from the bullseye—the true value. This systematic deviation represents bias.

Imprecision: Random Errors

• Definition: Imprecision refers to random variations in study results, often due to small sample sizes or high variability in measurements.

• Impact: Results are scattered, leading to uncertainty and inconsistency.

Example: If darts are randomly scattered across the dartboard, some close to the bullseye and others far away, it represents imprecision.

The Combined Effect of Bias and Imprecision

When both bias and imprecision are present, the results are not only inconsistent but also systematically incorrect. This significantly undermines the reliability of a study, making it difficult to extract meaningful conclusions.

Addressing Bias and Imprecision

• Reducing Bias: Improve study design, apply rigorous methodologies, ensure transparent reporting, and use the risk of bias assessments.

• Minimising Imprecision: Increase sample size, use more precise measurement tools, and apply GRADE criteria to assess evidence quality.

While systematic reviews aim for precision and reliability, precision alone is insufficient if the data is biased. By understanding and addressing both bias and imprecision, researchers can ensure their findings contribute to meaningful and trustworthy evidence.