Decreasing the probability of making a Type I error (rejecting a true null hypothesis) requires setting a lower significance level (α), which makes it harder to reject the null hypothesis. This increases the likelihood of failing to reject a false null hypothesis, thereby increasing the probability of making a Type II error (β).

Would you like further details or examples? Here are five related questions to consider:

1. What is the relationship between Type I and Type II errors in hypothesis testing?
2. How does sample size impact the probabilities of Type I and Type II errors?
3. What role does statistical power play in balancing Type I and Type II errors?
4. How can one minimize both Type I and Type II errors simultaneously?
5. What are the consequences of prioritizing the reduction of Type II errors over Type I errors in research?

Tip: Increasing the sample size is one way to reduce both Type I and Type II errors.