Type I vs Type II Errors: A Researcher’s Guide

Research Fundamentals #4

Type I Error (False Positive): Rejecting a true null hypothesis (H₀).

Example: A clinical trial concludes a new antidepressant medication is effective Concluding a drug is when it actually isn’t.
Controlled by Alpha (α), the significance level (e.g., 0.01).

Type II Error (False Negative): Failing to reject a false null hypothesis (H₀)

Example: Concluding a new antipsychotic medication has no effect when it actually does.
o Controlled by Beta (β), related to statistical power.

Balancing Errors

Reducing α decreases ▼ Type I errors but increases ▲ Type II errors.
Increasing ▲sample size helps reduce both errors.

Key Takeaways

• Trade-off exists: Lowering one error often increases the other.
• Type I Error (α): False positive – rejecting a true null hypothesis.
• Type II Error (β): False negative – failing to reject a false null hypothesis.
• **Larger sample sizes may improve accuracy and reduce both errors.

Critical Thinking Questions

1. Which error do you consider to be more of an offense? Why?
2. How can we incrementally control for false error?

Next Lesson: Statistical Power and Sample Size

In the next lesson, we will explore the concept of statistical power, how it relates to Type II error, and the importance of choosing an appropriate sample size in research studies.