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?"
The small web is beautiful
I believe that small websites are compelling aesthetically, but are also important to help us resist selling our souls to large tech companies. In this essay I present a vision for the “small web” as well as the small software and architectures that power it.
Why aim small?
Why aim small in this era of fast computers with plenty of RAM? A number of reasons, but the ones that are most important to me are:
- Fewer moving parts. It’s easier to create more robust systems and to fix things when they do go wrong.
- Small software is faster. Fewer bits to download and clog your computer’s memory.
- Reduced power consumption. This is important on a “save the planet” scale, but also on the very local scale of increasing the battery life of your phone and laptop.
- The light, frugal aesthetic. That’s personal, I know, but as you’ll see, I’m not alone.
Features and complexity
Niklaus Wirth of Pascal fame wrote a famous paper in 1995 called A Plea for Lean Software. His take is that “a primary cause for the complexity is that software vendors uncritically adopt almost any feature that users want”, and “when a system’s power is measured by the number of its features, quantity becomes more important than quality”.
Solving the problem of software bloat
But instead of just complaining, how do we actually solve this problem? Concretely, I think we need to start doing the following:
- Care about size: this sounds obvious, but things only change when people think they’re important.
- Measure: both your executable’s size, and your program’s memory usage. You may want to measure over time, and make it a blocking issue if the measurements grow more than x% in a release. Or you could hold a memory-reduction sprint every so often.
- Language: choose a language that has a chance.
- Remove: cut down your feature set. Aim for a small number of high-quality features. My car can’t fly or float, and that’s okay – it drives well.
- Say no to new features: unless they really fit your philosophy, or add more than they cost over the lifetime of your project.
- Dependencies: understand the size and complexity of each dependency you pull in. Use only built-in libraries if you can.
Raw size isn't enough
A few months ago there was a sequence of posts to Hacker News about various “clubs” you could post your small website on: the 1MB Club, 512KB Club, 250KB Club, and even the 10KB Club. I think those are a fun indicator of renewed interested in minimalism, but I will say that raw size isn’t enough – a 2KB site with no real content isn’t much good, and a page with 512KB of very slow JavaScript is worse than a snappy site with 4MB of well-chosen images.
...[Instead, it's about] an “ethos of small”. It’s caring about the users of your site: that your pages download fast, are easy to read, have interesting content, and don’t load scads of JavaScript for Google or Facebook’s trackers.