How many participants for a survey when you don’t know the total population?
I have been using a sample size calculator to know how many participants I should have to have a sample and results that are representative for my total population. But what if we don’t know the total population? how do you calculate your …
I’ve heard of tagging insights, but what do you think of tagging questions?
My team is kicking off a new project and we’re still in the discovery phase. We haven’t talked to users yet, so we have a lot of questions. I work with learning scientists and research scientists as well as user experience designers, so I …
How much should user research results be filtered for hostile stakeholders?
I’ve worked in organizations where there is ostensibly an appetite for user feedback, but bad news isn’t taken well. Some examples of reactions:
"There’s nothing we can do about that problem, let’s build it now and fix it later. This…
Should you Have a UX Research Portfolio?
Should you have a UX research portfolio? Many researchers don’t actively maintain them, but there’s a reason you might want to have one. Lawton Pybus, a lead user researcher at UserZoom shares recent survey results on this topic and some considerations for those who want to showcase their research projects.
Getting the Most Out of Surveys
The purpose of surveys and unmoderated research is to gain knowledge off the back of insights. To get insights, you need is accurate data. This isn’t always easy to come by. There is always the potential for bias to creep in and disrupt any meaningful results. By the time you have got your fingernails dirty by digging through reams of excel spreadsheets, it’s sometimes too late to spot bias, and this leads to invalid results and questions. This article takes a look at the common pitfalls associated with survey design, data collection, and analysis.
Five Research Personas to Watch Out For
In most circumstances, research participants are pleasant, helpful, and easy to work with. However, there are a few types that can make moderating a session really challenging and put you at risk of not getting the best insights. We only have a short time to gain feedback and insights from participants and certain personas can derail your research study. Based on over 20 years of experience conducting user research, I’ve pulled together a composite of participant characteristics that I’ve encountered into five personas to watch out for and how to effectively work with them.
Five Research Personas to Watch Out For
In most circumstances, research participants are pleasant, helpful, and easy to work with. However, there are a few types that can make moderating a session really challenging and put you at risk of not getting the best insights. We only have a short time to gain feedback and insights from participants and certain personas can derail your research study. Based on over 20 years of experience conducting user research, I’ve pulled together a composite of participant characteristics that I’ve encountered into five personas to watch out for and how to effectively work with them.
The Role of The Scientific Method & Intuition in UX
The scientific method is one of the more widely understood processes of obtaining evidence-based subject matter knowledge. It is a process of carefully and systematically collecting and evaluating empirical evidence to test ideas and answer questions. All of us at some point have felt that “AH-HA!” or the “eureka!” moment of sudden revelation or that “gut feeling” in a confusing or unsound situation. As UX professionals and decision-makers, it is important to note that the user experience is inseparable from the larger human experience and exercising scientific intuition becomes exceedingly important as we navigate the complex and evolving world around us.
Every Machine Learning Team Needs a UX Researcher
It’s doubtful that anyone would set out to build ML models that don’t meet their users’ needs, are biased, or inaccurate. Instead, teams likely find themselves here due to a general lack of knowledge. Individuals and companies focus on the technical tools and know-how to build ML and miss the equally crucial human side. What can be done to help prevent ML catastrophes? This article discusses the critical points and considerations in the ML workflow where UX research should be included.
Every Machine Learning Team Needs a UX Researcher
It’s doubtful that anyone would set out to build ML models that don’t meet their users’ needs, are biased, or inaccurate. Instead, teams likely find themselves here due to a general lack of knowledge. Individuals and companies focus on the technical tools and know-how to build ML and miss the equally crucial human side. What can be done to help prevent ML catastrophes? This article discusses the critical points and considerations in the ML workflow where UX research should be included.