What is a p-value, and how do you interpret it in the context of hypothesis testing?

Understanding the p-value is crucial for interpreting statistical results. Interviewers ask this question to assess your grasp of statistical concepts and how you apply them in decision-making. They want to see if you can articulate the role of p-values in hypothesis testing and whether you can distinguish between statistical significance and practical significance. A common misconception is that a low p-value proves a hypothesis to be true; however, it merely indicates the strength of the evidence against the null hypothesis. In real-world applications, correctly interpreting p-values can impact research findings, business decisions, and policy-making. Therefore, demonstrating a clear understanding of p-values and their implications is essential for roles that involve data analysis or research.