Data driven by corrupt data
Every time you analyse data, it’s useful to ask yourself what conclusions you can actually draw from it. However, it is often important to take a step back and check the data itself.
Here is an example somewhat anecdotal, but well illustrating the problem:
Imagine making any conclusions from the data obtained in this way. It does not matter what results we’ll get at the end of the experiment — the data is doomed anyway. We have no idea what customers meant by clicking on this buttons.
But if you think about it, even if you remove all the ambiguity from the interface of this questionnaire, we still won’t get accurate data. Now it will be the answers of only people who decided to press the button, which also does would not reflect reality.
In this case, to get the real data is very simple — just stand up with the counter, and calculate manually. Of course, this happens rarely, and we have to make all sorts of assumptions from the data. However, before you do them, it is important to make sure first that the data is generally suitable to do so.