False. Increasing the sample size (n) does not decrease the Type I error rate (α). The Type I error rate, which is the probability of rejecting a true null hypothesis, is determined by the significance level (α) set by the researcher before conducting the test. The sample size primarily affects the Type II error rate (β) and the power of the test, not the Type I error rate.

Would you like more details or have any questions?

Here are five related questions for further exploration:

1. How does increasing the sample size affect the power of a statistical test?
2. What is the relationship between Type I error rate and significance level (α)?
3. Can the Type I error rate be controlled by adjusting the significance level?
4. What is the difference between Type I and Type II errors in hypothesis testing?
5. How does sample size influence the confidence interval of a statistic?

Tip: When designing experiments, consider both Type I and Type II error rates to balance the risks of making incorrect decisions.