 Biostatistics. A lot of people have a tendency to get intimidated with biostatistics, but there's absolutely no need to. Hi, my name is Arun and I'm an instructor with MedSmarter. Taken from someone who has done a master's focused on biostatistics that's entirely learnable. The main thing is you take complex concepts and making them into more simple terms, for example. I'm a big golf and tennis guy, so it's like working on your golf swing or tennis swing. You break it out in more simple terms and then based off those simple terms and concepts, no matter how the questioner will ask you a question, you'll be able to answer it no matter which way. And that way you can answer any question with great confidence. Now let's get straight to it. Let's get to Biostats made easy. The first thing we're going to do is a 2x2 table. The thing about a 2x2 table is if you know the 2x2 table, you know everything or you will know everything. But if you do not know the 2x2 table, you will know nothing because you know nothing, John Snow. You know nothing, but if you do know the 2x2 table, you will know every single thing. So this is a 2x2 table. The main thing you want to remember is what's up top is reality. That's your R. That's actually what is true. What's out here is your test. This is a man-made test, right? So your man-made test tells you something is you have a disease, you're positive for it, or you are not positive for it. Here in reality, you have it, or in reality, you don't have it. Let's say it's diabetes. So let's say in reality you do have diabetes. In reality, you don't have diabetes. Here, your test will tell you if you do have diabetes and your test is telling you if you don't have diabetes. So if you do have diabetes and your test says you have diabetes, awesome. True positive. Here, if you don't have diabetes and your test says you don't have diabetes, then it's a true negative. So now you're all set, all right? But let's say that you don't have that. So the key here that you have to remember is everything is based on the test. What does that mean? That means that, let's say here, you don't have diabetes, but your test says that you do. The first thing you should think to yourself is this is a positive and this is a negative. Therefore, that is false. But what goes here? Go by the test. So it's false positive, all right? So then here, you do have diabetes, but your test says that you do not, all right? So that itself, this being a plus and this being a minus becomes a false. And what goes here? Think negative. Think it has to go by the test. So negative. So false negative. Therefore, true positive, false positive, false negative, and true negative. Because once you get this, you can build off everything really fast. Two by two square plus, minus, reality, test, plus, minus, plus and plus go together, true positive, minus and minus go together, true negative, plus and minus. You think to yourself, okay, well, that's a false. What test is it? Plus, false positive, plus and minus go together, false, what test is it? Negative. So you have your false positive and you have your false negative. For now, this will be a separate video later. But for now, just think to yourself, this is a type one error, which is false positive, also known as alpha. And your false negative is a type two error, also known as beta. How do you remember? Just think to yourself, one and A are both higher or above. So it's on the top here. And then two and B are lower. So naturally, they're lower. All right? So type one and alpha is up here. That's a type one error. And this is a type two error. And then beta. And don't get it confused as far as reality and what your test actually is. Because just remember, reality is up top. That's the truth. That's heaven. That's what's up top. And then your test is on the side, which is man made test. All right. So now you have your two by two. So now let's move on to sensitivity and specificity. All right? So here you have sensitivity. And here you have specificity. Note that it goes in alphabetical order, S E and S P. So sensitivity is here. And specificity is here. When you think about sensitivity and specificity, think vertical, okay? And we'll explain why in a second. But as far as sensitivity is concerned, you have to think to yourself, it's the actual, it's the proportion of actual people that have the disease or actual positives that are being measured as such. And specificity is a proportion of people that actually don't have the disease that are being measured as such. So based off that, your formula is true positive divided by true positive plus false negative. Remember, go right down this way. All right? So that's your sensitivity and always start with the true. Then your specificity is your true negative because you always start with the true. So you're going up here, specificity, true negative divided by true negative plus false positive. As far as positive predictive value, think horizontal, okay? So then you go this way. And negative predictive value horizontal, you go this way. The difference with positive predictive value and negative predictive value is you're actually seeing how good is your test. That means when your test comes out positive, how accurate is it? When your test comes out negative, how accurate is it? Again, you always start with the truths. So this would be true positive divided by true positive plus false positive. And then here, you would have your true negative divided by your true negative plus false negative. Something important to note here is the difference between sensitivity and let's say positive predictive value. All right? Sensitivity is a proportion of actual positives that are being measured as such. And when you think positive predictive value, it's based on your test. Okay? So this is reality and you're measuring the amount of people that actually have it versus this is based entirely on your test. So why do you need to know this? See, let's say you tell your patient that they have diabetes or AIDS. And the patient's going to say, well, how sure are you that I have diabetes or AIDS? And that's when you can say, well, your positive predictive value or the positive predictive value of the test, let's just come up with a number, is 95%. I'm 95% sure that the positive test that you got was accurate. We at MedSmart are here to help you score higher on your USMLE Step 1. And hopefully our personalized approach will serve you well. And we will help you achieve your dreams of becoming a physician. 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