What are type 1 and type 2 errors?

Type 1 and Type 2 Errors in Statistical Analysis

Type 1 error occurs when we incorrectly reject a true null hypothesis, while type 2 error occurs when we fail to reject a false null hypothesis. 1

Understanding Type 1 Errors

• Type 1 error (also called alpha error) happens when we conclude there is a difference or effect when in reality there isn't one - essentially a "false positive" finding 1
• The conventional threshold for type 1 error in medical research is typically set at 5% (P < 0.05), meaning we accept a 5% chance of incorrectly claiming an effect exists 1
• Type 1 errors are particularly concerning in clinical trials as they may lead to adoption of ineffective treatments or interventions 2
• In cardiovascular imaging, type 1 errors can result in misdiagnosis and potentially harmful interventions, such as unnecessary reoperation in patients with prosthetic heart valves 1

Understanding Type 2 Errors

• Type 2 error (also called beta error) occurs when we fail to detect a real difference or effect - essentially a "false negative" finding 1, 3
• The conventional acceptable rate for type 2 errors in medical research is typically 20% (corresponding to 80% power), which is notably higher than the accepted rate for type 1 errors 3
• Type 2 errors are particularly problematic when studies conclude "no difference exists" rather than the more accurate statement that "the null hypothesis could not be rejected" 3
• Many clinical studies fail to perform adequate power calculations before beginning research, increasing the risk of type 2 errors 4

Clinical Implications of Statistical Errors

• Type 1 errors can lead to implementation of ineffective or harmful treatments 2
• Type 2 errors may prevent beneficial treatments from being adopted in clinical practice 3, 4
• In diagnostic testing, these errors translate to false positives (type 1) and false negatives (type 2), both of which have significant clinical consequences 1
• The emphasis on avoiding type 1 errors (with P < 0.05 thresholds) may come at the cost of increased type 2 errors, potentially hindering medical progress 3

Strategies to Minimize Statistical Errors

• Researchers should perform a priori power calculations to ensure adequate sample sizes and reduce type 2 errors 4
• Multiple testing correction methods (such as Bonferroni, Hochberg, Holm, or Bretz approaches) can help control type 1 error rates when analyzing multiple outcomes 1
• Hierarchical testing strategies for secondary outcomes can help control type 1 error, though these approaches have limitations 1
• Moving beyond the rigid P < 0.05 threshold toward a more nuanced interpretation of statistical evidence can improve clinical decision-making 1

Common Pitfalls in Statistical Analysis

• Relying solely on P values without considering effect size, confidence intervals, and clinical importance can lead to misinterpretation of results 1
• "Method shopping" for statistical techniques that provide greater sensitivity at the cost of increased error rates 1
• Overinterpreting secondary outcomes without appropriate correction for multiple testing 1
• Failure to recognize that statistical non-significance does not prove equivalence - absence of evidence is not evidence of absence 3
• Confusing statistical significance with clinical significance can lead to implementation of treatments with minimal real-world benefits 1

Real-World Applications

• In clinical trials, type 1 errors may lead to approval of medications without meaningful benefits 1
• In diagnostic settings, type 2 errors may result in missed diagnoses and delayed treatment 1
• In research, understanding both error types helps in proper study design and interpretation of results 1
• For clinicians interpreting research, awareness of these error types is crucial for evidence-based practice 1