This assignment delves into inferential statistics, focusing on the significance of p < .05 in determining statistical significance and the associated pitfalls of hypothesis testing. It explores the reasons behind the widespread use of p < .05, tracing its origins to Fisher's convenient threshold for judging deviations. The discussion highlights the limitations of statistical significance, including its potential disconnect from practical significance, the risk of inappropriate hypothesis testing methods, and the impact of small sample sizes. Furthermore, it addresses publication bias and misinterpretations arising from uncorrected multiple testing. The assignment also examines the skepticism within medical fields regarding the strict application of p < .05 due to the high stakes and the need to minimize errors in statistical inference, referencing Cohen's critique of null hypothesis testing. Ultimately, the work underscores the importance of understanding the nuances and limitations of statistical significance and hypothesis testing in research.