(The two types are known as type 1 and type 2 errors.) Hypothesis tests based on statistical significance are another way of expressing confidence intervals (more precisely, confidence sets).
In other words, every hypothesis test based on significance can be obtained via a confidence interval, and every confidence interval can be obtained via a hypothesis test based on significance.
The first step in hypothesis testing is to set a research hypothesis.
In Sarah and Mike's study, the aim is to examine the effect that two different teaching methods – providing both lectures and seminar classes (Sarah), and providing lectures by themselves (Mike) – had on the performance of Sarah's 50 students and Mike's 50 students.
Since there are many facets to hypothesis testing, we start with the example we refer to throughout this guide.
Two statistics lecturers, Sarah and Mike, think that they use the best method to teach their students.
However, this is generally of only limited appeal because the conclusions could only apply to students in this study.
However, if those students were representative of all statistics students on a graduate management degree, the study would have wider appeal.
However, in order to use hypothesis testing, you need to re-state your research hypothesis as a null and alternative hypothesis.
Before you can do this, it is best to consider the process/structure involved in hypothesis testing and what you are measuring.