1. What is Type 2 Error?

Type 2 Error (β) occurs when a statistical test fails to reject a false null hypothesis. It represents the probability of incorrectly accepting the null hypothesis when the alternative hypothesis is true.

2. How Does the Calculator Work?

The calculator uses the Type 2 Error formula:

Where:

• \( \beta \) — Probability of Type 2 Error
• \( \text{Power} \) — Statistical power of the test

Explanation: Type 2 Error is the complement of statistical power. A test with 80% power has a 20% probability of making a Type 2 Error.

3. Importance of Type 2 Error Calculation

Details: Understanding Type 2 Error is crucial for experimental design, sample size determination, and interpreting statistical results. It helps researchers assess the risk of missing true effects.

4. Using the Calculator

Tips: Enter statistical power as a probability between 0 and 1. For example, 80% power should be entered as 0.8.

5. Frequently Asked Questions (FAQ)

Q1: What is the relationship between Type 1 and Type 2 errors?
A: Type 1 error (α) is rejecting a true null hypothesis, while Type 2 error (β) is failing to reject a false null hypothesis. They have an inverse relationship.

Q2: What is considered an acceptable Type 2 error rate?
A: Typically, researchers aim for β ≤ 0.2 (80% power), though this depends on the study context and consequences of missing an effect.

Q3: How can I reduce Type 2 error?
A: Increase sample size, use more sensitive measures, increase effect size, or use more powerful statistical tests.

Q4: What factors affect Type 2 error?
A: Sample size, effect size, variability in data, significance level (α), and test sensitivity all influence Type 2 error.

Q5: Is Type 2 error the same as false negative?
A: Yes, in medical testing, Type 2 error corresponds to a false negative result - failing to detect a condition that is present.