R Tutorial : Hypothesis Testing

Want to learn more? Take the full course at https://learn.datacamp.com/courses/experimental-design-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- A very important concept in experimental design is the formation and testing of a hypothesis or your central research question. For the Tooth Growth dataset we worked with previously, the hypothesis concerned the effect of different doses and administration methods of Vitamin C on the length of tooth growth in the guinea pig. Let's dig in a little more and look at how to create a research hypothesis. There are really two hypotheses that are grouped together: the null and alternative hypotheses. The null hypothesis is exactly what it sounds like, and the implications change depending on what you're testing. For example, in the tooth growth experiment, the null hypothesis is: "There is no effect of vitamin C dosage or administration type on guinea pig tooth growth." There's some nuance involved in the alternative hypothesis, and its construction will help lead you to the correct test. If you're testing if the mean is only less than or greater than a value (like you did in the first exercise), it's a one-sided test. If you're testing that it's not equal to some number, that's a two-sided test. The one/two-sided rule applies both to if you're testing one or two groups' means. Recall when we conducted a two-sided test to determine if the mean length of tooth growth was not equal to 18. The p-value was 0.4135, so at the 0.05 significance level, we fail to reject the null hypothesis. We have no strong evidence to suggest the mean is not equal to 18. Directly related to hypothesis testing is the idea of power. Power is the probability that the test correctly rejects the null hypothesis when the alternative hypothesis is true. One "golden rule" in statistics is to aim to have 80% power in your experiments, which you'll need an adequate sample size to ach