The importance of hypotheses
When you want to make a study, you first need to know what is the question you want to find an answer for. So you need to put some idea (based for example on observations or previous knowledge) to the test. Such an idea is called a hypothesis. The asked question should lead to testable predictions - the more specific they are, the fewer possibilities to explain the results (which is, of course, better for researchers). Eye-tracking test studies bring a huge amount of data, so it’s necessary to understand the problem to properly answer the research question.
You have two versions of your website design and you want to know which draws the most attention of viewers. You can show your testers both versions (in random order for each one) or make two studies, so two groups of testers and show each group one of the design and compare results.
Note: when testers freely browse a website higher number of fixation means that this area is more interesting, but if the testers are given a task to find something on the page the higher number of fixation could mean uncertainty and difficulties in recognizing the elements.
You want to find out the difference between novice and experienced drivers. Show these two groups the same set of images and analyze their eye movement measures and patterns to get information where they were looking most intensively and in what order (analyze recordings) and simply compare it.
There is some text to analyze if it’s understandable. Give it to your group of testers and analyze their eye movements. A hot point in some areas or frequent returns to a fragment of the text means that there were some difficulties in an understanding of the given text at that point.
There’s also a rule of thumb to make a pilot study - small-scale versions of the main study to make sure that the study is feasible. It consumes less resources (testers, time spent analyzing, etc.) than full study, but it gives a good notion about the hypothesis. Plus, it can undercover any pitfalls and mistakes in the early stadium of the project. It can also convince investors why this project is worth funding.
It’s also important to remember, that offset (number of tests that are failed) is about 15%. RealEye is aware of this fact and always orders about 15-20% testers more, so data it provides to you is without this offset. However, you have to remember that when you use your own testers you need to take it into account.