简而言之,您通常首先有一些假设您想测试,Axelson-Fisk教授说。你想看如果有你测量和一些变量之间的关系。你决定测试这个,所以你要画一个样本。现在,你必须决定什么是你想测试这个的人口,人口的样子。有尺度吗?很难从整个人口样本吗?等等。有一整套理论如何选择具有代表性。样品你需要多大取决于人口的变化有多大,和误差多大可以允许的。举例来说,如果你想声称药有效果,你可能想要比如果你更特定的测试更重要。确定你想要的越多,样品你应该更大。 So, the planning on how big margins you have and how big variance you have you have to decide on the sample size. There are of course methods for computing this as well, Prof Axelson-Fisk says. Next, depending on what question you are asking, you must choose analysis method, and model. Along with the model comes conditions that must be fulfilled. For example, common conditions are that the data must be normally distributed, it should be independently sampled, the variance must be homogeneous over your sample and so on, so you need to make sure that your data fulfills the conditions. If the data does not fulfill the conditions, either you must rethink how you sample your data, if your data can be transformed to meet the conditions, or you have to choose another method. Then, given what method you have chosen to analyze your data, there are lots of tools to help you to do the analysis and how to draw the conclusions. These are the main steps, Prof Axelson-Fisk says.