Research Fundamentals #2
(Everything you need to know)
Learning Objectives:
- Define alpha (α) and beta (β) levels in hypothesis testing.
- Understand their role in decision-making and statistical errors.
- Understand the trade-off between α and β.
- Identify appropriate levels for research standards.
What is Alpha (α)?
The alpha level (α) (significance level) represents the probability of making a Type I error—rejecting the null hypothesis (H₀) when it is actually true.
• Common α values:
α = 0.05 (Psychological Research)
α = 0.01 (Medical Research)
A lower Alpha (α) reduces the risk of false positives but increases the risk of false negatives.
What is Beta (β)?
The beta level (β) represents the probability of making a Type II error: failing to reject the Null Hypothesis (H₀) when the Alternative Hypothesis (H₁) is actually true.
The power of a test is defined as 1 – β, which is the probability of correctly rejecting the Null Hypothesis H₀ when the Alternative Hypothesis H₁ Is true.
Note: This is a prime example of why a smaller sample size may be a strength.-Dr. Santangelo
Commonly accepted power level: 0.80 (β = 0.20), meaning 20% chance of a Type II error.
Relationship Between Alpha(α) and Beta(β)
Reducing alpha level (α) (more conservative) increases beta level (β), (harder to detect a true effect).
Reducing beta level (β) (increasing power) often requires increasing alpha (α) or increasing sample size.
Researchers may elect to use a larger sample size. Understand increasing sample size to get a desired effect is often not ethically justifiable!
– Dr. Santangelo
How to Choose Alpha and Beta Levels
- Use α = 0.05 for psychological research where balancing Type I and Type II errors is important.
- Use α = 0.01 for medical research (e.g., medical trials) where false positives must be minimized.
- Use higher power (1 – β ≥ 0.80) when detecting small effects is important.
We will discuss effect size in another lesson. This is a gripe I have over the new writing of significance of P-Values. To be discussed in the next lesson plan. - Increase sample size to reduce both α and β without compromising significance.
Key Takeaways
- Alpha (α) controls the risk of Type I errors (false positives).
- Beta (β) controls the risk of Type II errors (false negatives).
- Lowering alpha (α) and beta (β) unless the sample size is increased.
- Balancing alpha (α) and beta (β) is crucial for designing robust research studies.
Next Lesson: P-Values and Statistical Significance
In the next lesson, we will explore p-values, how they relate to α, and their role in hypothesis testing. Stay tuned!