Python Tutorial: Student's t-test

Want to learn more? Take the full course at https://learn.datacamp.com/courses/experimental-design-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- It is possible to find all kinds of patterns in data. Some are expected, while others are more surprising. However, most datasets also include random variation. Knowing this, how can we go from a simple observation to a reliable result? Let's say we have the body weights of two samples from two groups of people, A and B. When we plot it, we seem to see a trend, where the group mean for sample B is larger than that for sample A. Is this difference real, or simply random variation? To draw a conclusion, we will need to distinguish between two cases or hypotheses. In statistics, our starting point is the "null hypothesis": that there isn't anything interesting happening and the observed patterns are just the product of random chance. With enough evidence, we can reject the null hypothesis and turn to the more interesting "alternative hypothesis": that the difference between these samples represents a real difference between the populations. But when do we know to reject the null hypothesis? Here we turn to two statistics. The p-value represents the likelihood that the distribution of values observed would occur if the null hypothesis were correct. We can't be 100 percent sure that our pattern couldn't have emerged due to random chance but we can quantify the probability that random chance would produce a given pattern; this is the p-value. The smaller the p-value is, the less likely it is that the null hypothesis can account for our observations. When p falls below a critical value, which we call alpha, we reject the null hypothesis. A standard value for alpha is 0 point 05. So, below a 5 percent probability that random chance would produce the pattern observed, it's usually considered safe to reject the null hypothesis. To compare two set