1. What is Type 1 Error?

Type 1 Error (α) occurs when a true null hypothesis is incorrectly rejected. It represents the probability of finding a statistically significant result when no true effect exists.

2. How Does the Calculator Work?

The calculator uses the simple relationship:

Where:

• \( \alpha \) — Type 1 error rate (significance level)

Explanation: The significance level (α) set by the researcher directly determines the probability of making a Type 1 error.

3. Importance of Type 1 Error

Details: Controlling Type 1 error is crucial in hypothesis testing to avoid false positive results and maintain the integrity of statistical conclusions.

4. Using the Calculator

Tips: Enter the significance level as a decimal value between 0 and 1 (e.g., 0.05 for 5% significance level).

5. Frequently Asked Questions (FAQ)

Q1: What is the relationship between α and Type 1 error?
A: The significance level α directly equals the probability of making a Type 1 error.

Q2: What are common α values used in research?
A: Common values are 0.05 (5%), 0.01 (1%), and 0.001 (0.1%), depending on the field and study requirements.

Q3: How does Type 1 error relate to p-values?
A: A p-value less than α indicates statistical significance, but also represents the probability of obtaining the observed result if the null hypothesis is true.

Q4: What is the trade-off between Type 1 and Type 2 errors?
A: Decreasing α reduces Type 1 errors but increases Type 2 errors (false negatives), and vice versa.

Q5: When should I use a stricter α level?
A: Use stricter α levels (e.g., 0.01 or 0.001) when the consequences of a false positive are severe or when conducting multiple comparisons.