Type I and Type II errors are statistical errors that occur in hypothesis testing, where a Type I error (false positive) involves rejecting a true null hypothesis, and a Type II error (false negative) involves failing to reject a false null hypothesis. Balancing these errors is crucial in research, as reducing one often increases the other, impacting the validity and reliability of study results.