Hicks Law

Hick's Law - Less Choice, Faster Decisions

Hick's Law (also Hick-Hyman Law) states that the time it takes to make a decision increases logarithmically with the number and complexity of choices. Named after British psychologist William Edmund Hick and American psychologist Ray Hyman (1952).

When to Use This Skill

• Designing navigation menus and information architecture
• Simplifying onboarding and setup flows
• Reducing form field options
• Prioritizing feature exposure
• Optimizing conversion funnels
• Planning dashboard layouts

Core Concepts

The Formula

RT = a + b * log2(n+1)

Where:
RT = Reaction time
a  = Time not involved in decision (physical movement, etc.)
b  = Empirical constant (~0.155s for choice tasks)
n  = Number of equally probable choices

Practical Impact

Choices	Relative Decision Time	User Experience
2	Baseline	Quick, confident
4	+1 unit	Still manageable
8	+2 units	Starting to slow
16	+3 units	Noticeable hesitation
32	+4 units	Overwhelm begins
64+	+5+ units	Paralysis likely

The Paradox of Choice

       User Satisfaction
            ^
            |      *
            |   *     *
            |  *        *
            | *           *
            |*              *____
            +-----------------------> Number of Choices
                 Sweet spot
                (4-7 items)

Analysis Framework

Step 1: Audit Decision Points

Map all places users must choose:

Screen/Flow	Decision Type	Options Count	Complexity
[Screen 1]	Navigation	[n]	[H/M/L]
[Screen 2]	Selection	[n]	[H/M/L]
[Screen 3]	Configuration	[n]	[H/M/L]

Step 2: Categorize Choices

Essential (keep)     Nice-to-have (maybe)     Remove
       |                    |                    |
       v                    v                    v
   [_______]            [_______]            [_______]
   [_______]            [_______]            [_______]
   [_______]            [_______]            [_______]

Step 3: Apply Reduction Strategies

1. Chunking: Group related items (3-4 per group)
2. Progressive disclosure: Hide advanced options
3. Smart defaults: Pre-select the common choice
4. Filtering: Let users narrow options
5. Recommendations: Highlight "Most Popular"

Output Template

## Hick's Law Analysis

**Interface/Flow:** [Name] **Analysis Date:** [Date]

### Decision Point Inventory

| Location  | Current Options | Target | Strategy             |
| --------- | --------------- | ------ | -------------------- |
| [Point 1] | [n]             | [n]    | [Chunk/Hide/Default] |
| [Point 2] | [n]             | [n]    | [Chunk/Hide/Default] |

### Reduction Plan

**Quick wins (no functionality loss):**

1. [Change 1]
2. [Change 2]

**Strategic reductions (requires tradeoffs):**

1. [Change with impact analysis]

### Expected Impact

- Decision time reduction: ~[X]%
- Conversion improvement: ~[X]% (estimated)
- Support ticket reduction: ~[X]% (estimated)

Real-World Examples

Example 1: Netflix vs. Cable

Cable TV: 500+ channels = Decision paralysis

• Users spend more time browsing than watching
• Satisfaction decreases despite more options

Netflix approach:

• Curated rows (chunking)
• "Top 10" highlights (social proof + reduction)
• "Because you watched..." (personalized filtering)
• Auto-play (eliminates decision entirely)

Example 2: In-N-Out Burger

Menu has only 4 items vs. competitors' 50+:

• Order time: 30 seconds vs. 2+ minutes
• Customer satisfaction: Higher
• Operation efficiency: Better

The constraint creates confidence in choice quality.

Example 3: Slack's Onboarding

Original: 15 configuration options upfront

• Completion rate: 62%
• Time to complete: 8 minutes

Redesigned: 3 essential questions, rest defaulted

• Completion rate: 89%
• Time to complete: 2 minutes

Best Practices

Do

• Aim for 5-7 options maximum in any grouping
• Use categorization to chunk larger sets
• Provide clear visual hierarchy
• Make the "default" choice obvious
• Offer search/filter for large option sets

Avoid

• Showing all features at once
• Flat menus with 10+ items
• Requiring decisions without clear benefit
• Equal visual weight for all options
• Removing options users actively need

When Hick's Law Doesn't Apply

• Expert users with learned shortcuts
• Emergency situations (trained responses)
• When options are not equally weighted
• Sequential vs. parallel choices

Reduction Techniques

1. Smart Defaults

Instead of:
[ ] Option A
[ ] Option B
[ ] Option C

Do:
[x] Option B (Recommended)
[ ] Option A
[ ] Option C

2. Progressive Disclosure

Basic Options
[Configure]

v Advanced (click to expand)
  [_] Setting 1
  [_] Setting 2

3. Chunking

Instead of 12 flat options:

Category A        Category B        Category C
- Item 1          - Item 5          - Item 9
- Item 2          - Item 6          - Item 10
- Item 3          - Item 7          - Item 11
- Item 4          - Item 8          - Item 12

Integration with Other Methods

Method	Combined Use
Progressive Disclosure	Hide complexity, reveal on demand
Cognitive Load	Fewer choices = lower cognitive burden
Fogg Behavior Model	Simpler choices increase ability
Jobs-to-be-Done	Focus options on user's actual job

Resources

• On the Rate of Gain of Information - Hick (1952)
• The Paradox of Choice - Barry Schwartz
• Don't Make Me Think - Steve Krug