Research & Ethnography
What is unspoken
A nested classificatory hierarchy
Unfinished
Record them all
Recommendations for field notes
In Defense of Browsing
The illustrated guide to a Ph.D.
When users never use the features they asked for
An Article by Austin Z. HenleyWe deployed our tool. Almost no one used it.
The handful that did use it, used it once or twice and barely interacted with it. After a few days, zero people were using it.
Why did they tell me they wanted these features?
Why Most Published Research Findings Are False
A Research Paper by John P.A. IoannidisThere is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance.
How can we develop transformative tools for thought?
A Research Paper by Andy Matuschak & Michael NielsenConventional tech industry product practice will not produce deep enough subject matter insights to create transformative tools for thought.
...The aspiration is for any team serious about making transformative tools for thought. It’s to create a culture that combines the best parts of modern product practice with the best parts of the (very different) modern research culture. You need the insight-through-making loop to operate, whereby deep, original insights about the subject feed back to change and improve the system, and changes to the system result in deep, original insights about the subject.
A hypothesis is a liability
A Research Paper by Itai Yanai & Martin LercherThere is a hidden cost to having a hypothesis. It arises from the relationship between night science and day science, the two very distinct modes of activity in which scientific ideas are generated and tested, respectively [1, 2]. With a hypothesis in hand, the impressive strengths of day science are unleashed, guiding us in designing tests, estimating parameters, and throwing out the hypothesis if it fails the tests. But when we analyze the results of an experiment, our mental focus on a specific hypothesis can prevent us from exploring other aspects of the data, effectively blinding us to new ideas.
Keep digging
An Article by Ryan SingerThe hardest thing about customer interviews is knowing where to dig. An effective interview is more like a friendly interrogation. We don’t want to learn what customers think about the product, or what they like or dislike — we want to know what happened and how they chose... To get those answers we can’t just ask surface questions, we have to keep digging back behind the answers to find out what really happened.
Fast Path to a Great UX – Increased Exposure Hours
An Article by Jared SpoolAs we’ve been researching what design teams need to do to create great user experiences, we’ve stumbled across an interesting finding. It’s the closest thing we’ve found to a silver bullet when it comes to reliably improving the designs teams produce.
The solution? Exposure hours. The number of hours each team member is exposed directly to real users interacting with the team’s designs or the team’s competitor’s designs. There is a direct correlation between this exposure and the improvements we see in the designs that team produces.
Weighing up UX
An Article by Jeremy KeithMetrics come up when we’re talking about A/B testing, growth design, and all of the practices that help designers get their seat at the table (to use the well-worn cliché). But while metrics are very useful for measuring design’s benefit to the business, they’re not really cut out for measuring user experience.
Monkeys testing random designs
A Tweet by Jared SpoolA/B testing is an effective approach to use science to design and deliver deeply-frustrating user experiences.
A/B testing without upfront research is just random monkeys testing random designs to see which of those designs do “best” against random criteria.
If drug testing was actually implemented like most A/B tests, you’d give 2 drugs to 2 groups of people and pick the “winner” by whichever group had fewer deaths.
You and Your Research
A Speech by Richard HammingThis talk centered on Hamming's observations and research on the question "Why do so few scientists make significant contributions and so many are forgotten in the long run?"
Pair Design: Better Together
Pair design is the counterintuitive practice of getting more and better UX design done by putting two designers together as thought partners to solve design problems. It’s counterintuitive because you might expect that you could split them up to work in parallel to get double the design done, but for many situations, you’d be wrong. This document will help explain what pair design is, how it works, and tour through the practicalities of implementing it in your practice.
It involves two brains
It involves two brains on a project at the same time. This doesn’t mean part time, checking in with each other on work that’s been accomplished separately.
Pair design really means being in the same room, working on the same problem, with both brains focused on the problem simultaneously for the duration of the project.
A distinct and complementary stance
Each person in the pair takes a distinct and complementary stance toward the design problem as they work together. One generates solutions. That is, one individual materializes solutions to the problem at hand for discussion and iteration. The other synthesizes the proposed solutions.
Gens and synths
Gens are generally comfortable drawing and drawing in front of their partner. Additionally, the generator needs to have “fearless generativity,” to be able to come up with a dozen pretty good solutions to a problem even with incomplete information.
Designers in the synthesizer role need to be skilled at describing designs and explaining rationale in writing. The role requires the designer to be detail oriented and have a strong memory, to keep the big picture of the system, stakeholders, and users in mind as a reference for designs on the table.
We come as a team
There is a legend at Cooper of one team who found pairing with each other so powerful and fruitful that when they left that company, they sought out opportunities and even interviewed at other organizations as a pair.
Starting off with pair design
It’s better to start small. Find the “genniest” designer you can and pair her with the “synthiest,” have them work through a few projects as a pair to see how it goes, evolve a process that works for your organization, smooth out the wrinkles, and become resident experts. Then, split them up, assign them with new pairs, and begin to spread.
What are the benefits of pair design?
It Makes for Better Design
- Pairing forces constant iteration: idea testing and course-correction.
- It brings to bear two brains and two stances.
It Makes for Better Designers and Better Design Organizations
- They are happier.
- Pair design makes it easier to focus on core aptitudes.
- They cross-pollinate: a mechanism for a learning organization.
Pair Design Makes for a More Effective Process
- Pairing avoids the problem of dueling whiteboards.
- It encourages designers to materialize ideas early.
- It encourages designers to vocalize their rationale.
- It encourages constant course-correction.