R Programming for Statistics and Data Science - Type I and Type II Errors

The video tutorial explains Type 1 and Type 2 errors in statistics, using a monster hunting analogy. A Type 1 error, or false positive, occurs when a true null hypothesis is rejected, while a Type 2 error, or false negative, happens when a false null hypothesis is accepted. The video discusses the significance level (alpha) and the probability of making a Type 2 error (beta), emphasizing the importance of sample size and population variance. It also covers the concept of statistical power, which is the probability of rejecting a false null hypothesis, and how researchers can increase it by enlarging the sample size.

Explain the significance of alpha in the context of Type 1 errors.

What factors influence the probability of making a Type 2 error?