The p value is used in hypothesis testing to decide if a hypothesis should be rejected. Type I error happens when the null hypothesis is wrongly rejected, and Type II error occurs when the null hypothesis is not rejected even though it is false. Researchers set a level of significance to determine what is considered significant. To avoid errors, it is important to have enough statistical power to detect differences. However, relying solely on p values and sample size can lead to imprecise results. It's crucial to understand these errors when making statistical decisions.