Today, we’d do a fun exploration of statistics, tackling a concept that is both familiar and yet frequently misconstrued – the elusive, yet ever present, p-value. Don’t worry if you’ve found yourself scratching your head over it before; I’m here to break it down in hopefully an engaging and clear way.
Before we go deeper, lets start with a relatable scenario:
Imagine starting out as a freshly graduated data scientist, looking for your first job, you’ve done your due diligence, invested countless hours conquering coding challenges like leet code, and mastered intricate concepts of machine learning algorithms, you’re prepared and confident for your very first job interview. The interviewer is welcoming, the atmosphere is inviting, and the questions appear within your knowledge base, and then they ask you: “What exactly is a p-value?”
While you’ve encountered the term previously, your response in the moment might have been something like, “It indicates the significance of our hypothesis.” However, as the interviewer digs further, you realize you might be diving into deeper waters than anticipated. If this scenario sounds familiar, rest assured – you’re not alone. In this blog post, we’d attempt to genuinely try to deconstruct what a p-value is and what it isn’t. We’ll do so, step by step, so that the next time you encounter this concept, you’ll possess proper understanding of the concept.
At its heart, the term “p-value” stands for “probability value.” Yet, believe me, its significance is far from straightforward. This concept can prove to be a bit unintuitive and difficult to grasp, primarily due to common misconceptions and even misuse in the industry.
Picture a fictional pharmaceutical company, MM Pharmaceuticals, introducing “Drug Alpha” as a remedy for headaches. The question at hand: does Drug Alpha genuinely alleviates headaches? To scrutinize its efficacy, MM Pharmaceuticals conduct a study involving two groups — one receives Drug…