The text discusses the challenges of measuring performance in software development, particularly when making changes that may affect performance. It highlights the importance of regular testing for performance regressions and the need to interpret measurements accurately. The authors introduce the concept of statistical hypothesis testing, specifically the Student's t-test, to determine if changes have a statistically significant impact on performance. This approach helps to avoid human bias and provides a way to objectively evaluate changes, making it easier to "love low p-values" and confidently reject null hypotheses.