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?"
On the "Building" of Software and Websites
I’m beginning to suspect that software, and more conspicuously the Web, is fundamentally the wrong shape for the archetype of the construction project.
You are agreeing to make a Thing
When you sign the contract for the construction project, you are agreeing to make a Thing—app, website, whatever. And you will have agreed to deliver this Thing on a certain date, also known as a deadline. From this point forward, the goals of shipping the Thing on time and actually solving the client’s problem will be in competition with each other.
The Thing-deadline calculus
Now, I understand deadlines. I understand that the plane will take off whether or not I’m on it, or the importance of beating the holiday retail rush, or that "the show must go on". It is perfectly clear to me how people use timekeeping technology to coordinate social activity. It’s actually quite remarkable when you step back and look at it. But, over the years, I have observed that there is a difference between those examples and the ones around the delivery of Things, which tend to be completely arbitrary. When you wrap an arbitrarily complex endeavor up in a neat launch date, the goal seems to be more about coercing the people beneath you to absorb the overhead of all the details you left out—that or sweating it yourself. As a tool for coordinating human activity, I have come to believe that the Thing-deadline calculus is, considering more sophisticated alternatives, unnecessarily crude.
Trees and graphs
A tree is a kind of graph, but a graph can be considerably more complex than a tree.
I have reason to believe, which for brevity’s sake I will treat elsewhere, that the most complex class of processes and structures we humans can consciously prescribe, reduces mathematically to a tree. A tree has a top, bottom, left and right. Its branches fan out from the trunk and they don’t intersect with one another. They are discrete, contiguous, identifiable objects which persist across time. Trees are Things.
Software and websites, however, reduce to arbitrarily more complex structures: they are graphs. A graph has no meaningful orientation whatsoever. No sequence, no obvious start or end—at least none that we can intuit. It is better considered not as one Thing, but as a federation of Things, like the brain or a fungus network, or perhaps a composite artifact left behind from an ongoing process, like an ant colony or human city.
Content as value
The most important consideration for any software or web excursion is content: the content of the text and other communicative media, as well as the content of the code that executes the business processes. The ability to tick off a page or piece of functionality as being done only produces a nominal successful result; the careful crafting of what one of these objects says produces a real one.