![calculate standard error based on ssr and sst calculate standard error based on ssr and sst](https://d2vlcm61l7u1fs.cloudfront.net/media/86f/86fe8874-f73e-462f-8af5-a1b7ca0ebfce/phpLmK2ku.png)
I’m so introverted that I have extreme anxiety in social situations that warrant a therapy dog by the name of Chloe. If the two groups are different sizes or you are comparing two separate event means, then you conduct a Independent Sample t-test. If your two groups are the same size and you are taking a sort of before-and-after experiment, then you will conduct what is called a Dependent or Paired Sample t-test. The type of t-test that you may need depends on the type of sample that you have. Of course, different types of groups and setups call for different types of tests. There are a bunch of cases in which you may want to compare group performance such as test scores, clinical trials, or even how happy different types of people are in different places. The t-test is a test statistic that compares the means of two different groups. For example, do the clothes look significantly different on the mannequin than they do on you? Let’s take a look at the two most common types of test statistics: t-test and F-test. Most often, test statistics are used to see if the model that you come up with is different from the ideal model of the population. Test statistics calculate whether there is a significant difference between groups. However, experimental models are concerned with cause-effect models, or at least models that state a significant difference between cases.
![calculate standard error based on ssr and sst calculate standard error based on ssr and sst](https://image3.slideserve.com/5657635/standard-error-of-estimate-l.jpg)
Linear regression, multiple regression, and logistic regression are all types of linear models that correlate variables that occur simultaneously. In another post, I discussed the nature of correlational and experimental research. The test-statistic tells you if the difference between them (because I definitely do not look like the mannequin.) is significant. When you get home, you test them out and see how they actually look (the data-based model).
![calculate standard error based on ssr and sst calculate standard error based on ssr and sst](https://media.cheggcdn.com/media%2F4a6%2F4a6f06e2-1d8b-4740-97ca-a4b5000404b3%2FphpguLkxS.png)
When you are in the store, the mannequin tells you how the clothes are supposed to look (the theoretical model). The goal of a test statistic is to determine how well the model fits the data. But how good are we at that? I mean, numbers are only good for so many things, right? How do we know if they are telling the right story?Įnter the famous world of test statistics. Statistics is all about coming up with models to explain what is going on in the world. By John Clark on Januin Inferential Statistics, Sampling and Experimental Design