So many people expect specific outcomes before conducting research. And then they feel as if they lost when the results of their tests do not match the expectations they had. While it might be tough to handle, disproving your own idea can be part of successful research.
Let me give you a short explanation of why you do not lose if you prove the opposite of what you originally thought. To do this, I will describe two real-life scenarios that highlight the problem and incorrect approach towards it.
Proving the opposite
When you are starting an experiment, you have a question you want answered. This can be done in a form of having a hypothesis. You can create a statement that you test to see whether it is valid one.
One of my friends decided to write a thesis about people’s behaviour when playing games. He wanted to map personality traits to differences in controlling a game using a mouse. This experiment had three parts.
The first step was determining person’s traits. He used a personality test to get this data. The second step was creating a simple game that could be controlled in multiple ways. Third step was evaluating the data and finding a correlation.
He found non. And it was a major problem for him. He could not see a single connection between people’s traits and their behaviour when controlling a game. He tried everything. He even ran the experiment again. And still nothing.
All in all, he decided to wait a year to look at it again. And he still did not find a correlation. He has never finished the thesis. But in reality, the valid outcome has been there the whole time – there is no correlation between the measured variables.
His experiment proved that personality traits do not affect how people use mouse when playing a specific genre of games that he prepared. And who knows, maybe this extends to all games. And yet, he did not see that as a success. He eventually gave up and never published the results.
Forcing the outcome
I had a luck of being interviewed in a guerrilla interview at a central station in Berlin. Two young people – who appeared to be students – walked up to me and wanted to ask me a couple of questions. As an UX Researcher, I was thrilled with this opportunity and so I agreed to participate.
The very first question stated their goal – prove that advertisement boards on the station were useful. Apparently, there was a new feature and from time to time the ads boards also showed news sites and their articles. I personally see all those ads as spam and ignore them. Therefore, I did not notice the news either. The two young students became visibly unpleased by my opinion and experience the very moment I stated it.
They kept asking leading questions, trying to force me to find any kind of a value in those news. All in vein, as I kept saying I did not notice it. So, they started asking hypothetical questions and became interested in knowing if I would google the news or visit displayed news sites. I must admit, they almost swayed me to start questioning myself and actually say I might google it.
Luckily, I am a researcher and an asshole, and I know that since I have not notice those ads nor news, I would have not taken any action. I was simply not interested in it. Even if I knew about the news, they were in different language and I already have my own way of getting news, I do not want or need more. So, I kept saying no. They tried to persuade me and I became quite amused by it.
I bet though, that majority of people would believe – even if not consciously – that they can find value in mentioned news, just based on how the questions were structured and because it was so painfully obvious the students wanted to hear it. That would mean they would gather biased and incorrect data.
Take a step back. Do not rely on your hypothesis to be the absolutely valid one. After all, you are there to research the idea and find out the real truth. Research done to prove a thing at all costs is a horrible approach, not even worthy of being called a research.
Do not influence your research participants, otherwise you will have incorrect data. Do not give up on correctly gathered results just because they did not match your expectations of how things work. Embrace those results and show it to your superiors and the world.