I still remember a learner from my Saturday batch staring at a form with fifteen fields and four buttons, whispering, “Where do I even start?” That moment is exactly why Hick’s Law matters. It gives us a simple, evidence-backed idea: the more choices we show, the longer people take to decide—and sometimes they freeze and do nothing at all.
In this article, I’ll keep the language plain, the advice practical, and the tone human. We’ll cover what Hick’s Law says, where it helps (and where it doesn’t), and exactly how to apply it in interfaces so your users can breathe—and move.
Hick’s Law (also called the Hick–Hyman Law) says that decision time increases as the number of choices increases. In the original lab studies, researchers measured how long it took people to react when the number of possible signals (and matching responses) increased. Mathematically, average reaction time tends to grow with the logarithm of the number of options—often written as “decision time ≈ a + b × log₂(number of choices).”
If equations make your eyes glaze over, here’s the intuition: doubling the number of choices does slow people down, but not in a straight line—more like steps. Still, the core takeaway is that more choice equals more time. In UX, that extra time can mean confusion, drop-offs, or abandoned carts.
Hick’s Law doesn’t say “always remove options.” It advises being intentional about how and when you present options. Tangible interfaces are messy: tasks differ, users differ, context matters. Long menus can still work when they’re organized well, labeled clearly, and revealed progressively. Even
Nielsen Norman Group (NN/g) emphasizes that combining Hick’s Law with techniques such as grouping, chunking, and progressive disclosure can make long menus more straightforward to use.
Also, we shouldn’t confuse Hick’s Law with “choice overload” claims in marketing. The famous jam study (6 jams vs. 24) suggests that fewer options can drive more purchases—but modern researchers also warn that the effect varies by context and the presence of default options. Treat “less is more” as a strong hypothesis to test, not a law of nature.
And yes, the evidence base is real. Hick’s original work and decades of follow-ups show robust relationships between the number of stimulus–response alternatives and response time (with nuances we’ll respect in the applications below).
Here’s my field-tested playbook, the one I use with teams at UXGen Studio.
A quick story from my practice: we redesigned a B2B dashboard that consolidated 18 reports into a single, flat list. We moved the five “most used” into a Featured band, tucked the rest behind a “Browse all reports” drawer, and added a search field. Support tickets about “can’t find reports” quietly vanished. That’s Hick’s Law done respectfully—without deleting power, we just staged it.
This style of measurement aligns with the spirit of the lab literature (choice count ↔ decision time) while accommodating real-world product teams.
When we collaborate with a company, we don’t just preach “simplify.” We co-design decisions.
If you’re a startup, we’ll do a one-week sprint to fix your navigation and purchase flow. If you’re an enterprise, we’ll stage it across journeys (onboarding, search, checkout) so risk stays low while impact remains high.
Q1. Is Hick’s Law the same as the “paradox of choice”?
Not exactly. Hick’s Law is concerned with decision time as the number of options increases. “Choice overload” refers to the impact on satisfaction and the likelihood of making a choice. They overlap, but they’re different questions. Use Hick’s Law to tame time-to-decide; test overload when you care about the possibility to act.
Q2. So should I always cut options?
No. Sometimes users need breadth. The move is to stage and label options so the first step feels obvious. Long lists can work when grouped and searchable.
Q3. What’s a safe number of tabs on mobile?
Apple’s guidance: about 3–5 tabs for iPhone (you may use a few more on iPad if necessary). It’s guidance, not a rule, but it aligns with Hick’s Law in spirit.
Q4. Do defaults help?
Yes—especially in larger choice sets, people tend to gravitate toward clear defaults. Design defaults carefully and explain why they’re recommended.
Q5. How do I demonstrate to my stakeholders that “simplify” is effective?
Instrument decision time and task success. Run an A/B where Variant A reduces visible choices (grouping/disclosure) and Variant B keeps choices but clarifies labels. Pick the winner by data, not taste.
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Prepared by: UXGen Design Studio
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