How recency bias makes a big impact on UX data?

It is easier for a human being to remember the last things that came up in any conversation, debate or discussion because it’s the most recent. It is because the human brain’s short-term memory is attributed to the readout of the last few items of the conversation. The same can happen when UX researchers conduct a series of usability testing, they might focus more on the problems found in the latest session or last task. UX practitioners who suffer from recency bias tend to make their opinions towards the latest news. This bias is also called as “Position effect”.

This can happen in both ways -

1. When participants choose the last things amongst the options the researcher provides-

Example — Once a researcher set up the table with four pairs of ladies’ stockings labelled A, B, C and D from left to right. They asked customers to select the best one out of four. Most people (40%) preferred D, and the fewest people (12%) preferred A. But there’s a twist. All the pairs of stockings were identical. The reason most people preferred D was simply because of its sequence.

2. When researchers pick the last words said by the participants-

In qualitative research settings, it may happen that researchers cheery pick the most recent quotes mentioned by participants and then interpret the insights around them.

Example — It commonly appears in employee evaluations, as a distortion in favour of recently completed activities or recollections.

Ways to overcome the Recency Bias-

1. Task sequence -Try to assign tasks to the participants in a different sequence. This way you have the best chance to observe first-time user experiences for all your tasks, and you are less vulnerable to sequence bias.
2. Task Assignment -Assigning different tasks to different people (in-between research). For example, if you want to take opinions on 3 different things, you can recruit 3 participants and assign one thing to each of the participants and take their opinion on it.
3. Randomizing cards -In card sorting, this bias can lead to users omitting or ignoring the middle elements, which might hamper the effectiveness of the activity/experiment. You can avoid this by changing the arrangement of those elements for participants.
4. Detailed notes -Make a habit of taking detailed notes or recordings for each interview, observation or conversation you are having with the participant. This way, you can review what the participant said at the start of the conversation in case you don’t remember.

Arranging the research items in a random manner helps to nullify the bias. Items can be the set of tasks, recruited participants, handwritten notes, interview questions or sticky notes depending on the type of research you are doing.

Final Words -

I think researcher should make their own list of “bias heuristics” and check whether they are suffering from any type of bias at each phase of the research process. It is a good practice to acknowledge and avoid bias before it hampers your data.

Thanks for reading! Stay tuned to know more about the different types of bias and ways to overcome those.

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