format: apply repo prettier (3.8.1) to the folded skills tree

963 markdown files reformatted with the repository's pinned prettier so pnpm format:check covers the folded tree like every other repo file. The formatter's embedded-language pass also normalized code fences (TS semicolons, closed HTML tags in examples, lowercased CSS hex colors, one renumbered list that skipped an index). Alphanumeric token deltas vs the fold commit were audited file-by-file; all are formatter-equivalent markup normalizations plus the four sanitized skills.
2026-08-19 14:37:17 -05:00
parent d2eeb64433
commit 1a822493ba
962 changed files with 29594 additions and 27188 deletions
@@ -24,20 +24,24 @@ Before designing a test, understand:
 ## Core Principles

 ### 1. Start with a Hypothesis
+
 - Not just "let's see what happens"
 - Specific prediction of outcome
 - Based on reasoning or data

 ### 2. Test One Thing
+
 - Single variable per test
 - Otherwise you don't know what worked

 ### 3. Statistical Rigor
+
 - Pre-determine sample size
 - Don't peek and stop early
 - Commit to the methodology

 ### 4. Measure What Matters
+
 - Primary metric tied to business value
 - Secondary metrics for context
 - Guardrail metrics to prevent harm
@@ -66,12 +70,12 @@ We'll know this is true when [metrics].

 ## Test Types

-| Type | Description | Traffic Needed |
-|------|-------------|----------------|
-| A/B | Two versions, single change | Moderate |
-| A/B/n | Multiple variants | Higher |
-| MVT | Multiple changes in combinations | Very high |
-| Split URL | Different URLs for variants | Moderate |
+| Type      | Description                      | Traffic Needed |
+| --------- | -------------------------------- | -------------- |
+| A/B       | Two versions, single change      | Moderate       |
+| A/B/n     | Multiple variants                | Higher         |
+| MVT       | Multiple changes in combinations | Very high      |
+| Split URL | Different URLs for variants      | Moderate       |

 ---

@@ -79,14 +83,15 @@ We'll know this is true when [metrics].

 ### Quick Reference

-| Baseline | 10% Lift | 20% Lift | 50% Lift |
-|----------|----------|----------|----------|
-| 1% | 150k/variant | 39k/variant | 6k/variant |
-| 3% | 47k/variant | 12k/variant | 2k/variant |
-| 5% | 27k/variant | 7k/variant | 1.2k/variant |
-| 10% | 12k/variant | 3k/variant | 550/variant |
+| Baseline | 10% Lift     | 20% Lift    | 50% Lift     |
+| -------- | ------------ | ----------- | ------------ |
+| 1%       | 150k/variant | 39k/variant | 6k/variant   |
+| 3%       | 47k/variant  | 12k/variant | 2k/variant   |
+| 5%       | 27k/variant  | 7k/variant  | 1.2k/variant |
+| 10%      | 12k/variant  | 3k/variant  | 550/variant  |

 **Calculators:**
+
 - [Evan Miller's](https://www.evanmiller.org/ab-testing/sample-size.html)
 - [Optimizely's](https://www.optimizely.com/sample-size-calculator/)

@@ -97,19 +102,23 @@ We'll know this is true when [metrics].
 ## Metrics Selection

 ### Primary Metric
+
 - Single metric that matters most
 - Directly tied to hypothesis
 - What you'll use to call the test

 ### Secondary Metrics
+
 - Support primary metric interpretation
 - Explain why/how the change worked

 ### Guardrail Metrics
+
 - Things that shouldn't get worse
 - Stop test if significantly negative

 ### Example: Pricing Page Test
+
 - **Primary**: Plan selection rate
 - **Secondary**: Time on page, plan distribution
 - **Guardrail**: Support tickets, refund rate
@@ -120,14 +129,15 @@ We'll know this is true when [metrics].

 ### What to Vary

-| Category | Examples |
-|----------|----------|
-| Headlines/Copy | Message angle, value prop, specificity, tone |
-| Visual Design | Layout, color, images, hierarchy |
-| CTA | Button copy, size, placement, number |
-| Content | Information included, order, amount, social proof |
+| Category       | Examples                                          |
+| -------------- | ------------------------------------------------- |
+| Headlines/Copy | Message angle, value prop, specificity, tone      |
+| Visual Design  | Layout, color, images, hierarchy                  |
+| CTA            | Button copy, size, placement, number              |
+| Content        | Information included, order, amount, social proof |

 ### Best Practices
+
 - Single, meaningful change
 - Bold enough to make a difference
 - True to the hypothesis
@@ -136,13 +146,14 @@ We'll know this is true when [metrics].

 ## Traffic Allocation

-| Approach | Split | When to Use |
-|----------|-------|-------------|
-| Standard | 50/50 | Default for A/B |
-| Conservative | 90/10, 80/20 | Limit risk of bad variant |
-| Ramping | Start small, increase | Technical risk mitigation |
+| Approach     | Split                 | When to Use               |
+| ------------ | --------------------- | ------------------------- |
+| Standard     | 50/50                 | Default for A/B           |
+| Conservative | 90/10, 80/20          | Limit risk of bad variant |
+| Ramping      | Start small, increase | Technical risk mitigation |

 **Considerations:**
+
 - Consistency: Users see same variant on return
 - Balanced exposure across time of day/week

@@ -151,11 +162,13 @@ We'll know this is true when [metrics].
 ## Implementation

 ### Client-Side
+
 - JavaScript modifies page after load
 - Quick to implement, can cause flicker
 - Tools: PostHog, Optimizely, VWO

 ### Server-Side
+
 - Variant determined before render
 - No flicker, requires dev work
 - Tools: PostHog, LaunchDarkly, Split
@@ -165,6 +178,7 @@ We'll know this is true when [metrics].
 ## Running the Test

 ### Pre-Launch Checklist
+
 - [ ] Hypothesis documented
 - [ ] Primary metric defined
 - [ ] Sample size calculated
@@ -175,16 +189,19 @@ We'll know this is true when [metrics].
 ### During the Test

 **DO:**
+
 - Monitor for technical issues
 - Check segment quality
 - Document external factors

 **DON'T:**
+
 - Peek at results and stop early
 - Make changes to variants
 - Add traffic from new sources

 ### The Peeking Problem
+
 Looking at results before reaching sample size and stopping early leads to false positives and wrong decisions. Pre-commit to sample size and trust the process.

 ---
@@ -192,6 +209,7 @@ Looking at results before reaching sample size and stopping early leads to false
 ## Analyzing Results

 ### Statistical Significance
+
 - 95% confidence = p-value < 0.05
 - Means <5% chance result is random
 - Not a guarantee—just a threshold
@@ -207,18 +225,19 @@ Looking at results before reaching sample size and stopping early leads to false

 ### Interpreting Results

-| Result | Conclusion |
-|--------|------------|
-| Significant winner | Implement variant |
-| Significant loser | Keep control, learn why |
+| Result                    | Conclusion                       |
+| ------------------------- | -------------------------------- |
+| Significant winner        | Implement variant                |
+| Significant loser         | Keep control, learn why          |
 | No significant difference | Need more traffic or bolder test |
-| Mixed signals | Dig deeper, maybe segment |
+| Mixed signals             | Dig deeper, maybe segment        |

 ---

 ## Documentation

 Document every test with:
+
 - Hypothesis
 - Variants (with screenshots)
 - Results (sample, metrics, significance)
@@ -231,16 +250,19 @@ Document every test with:
 ## Common Mistakes

 ### Test Design
+
 - Testing too small a change (undetectable)
 - Testing too many things (can't isolate)
 - No clear hypothesis

 ### Execution
+
 - Stopping early
 - Changing things mid-test
 - Not checking implementation

 ### Analysis
+
 - Ignoring confidence intervals
 - Cherry-picking segments
 - Over-interpreting inconclusive results
@@ -16,6 +16,7 @@ Reference for calculating sample sizes and test duration.
 **Baseline conversion rate**: If your page converts at 5%, that's your baseline.

 **MDE (Minimum Detectable Effect)**: The smallest improvement you care about detecting. Set this based on:
+
 - Business impact (is a 5% lift meaningful?)
 - Implementation cost (worth the effort?)
 - Realistic expectations (what have past tests shown?)
@@ -30,53 +31,53 @@ Reference for calculating sample sizes and test duration.

 ### Conversion Rate: 1%

-| Lift to Detect | Sample per Variant | Total Sample |
-|----------------|-------------------|--------------|
-| 5% (1% → 1.05%) | 1,500,000 | 3,000,000 |
-| 10% (1% → 1.1%) | 380,000 | 760,000 |
-| 20% (1% → 1.2%) | 97,000 | 194,000 |
-| 50% (1% → 1.5%) | 16,000 | 32,000 |
-| 100% (1% → 2%) | 4,200 | 8,400 |
+| Lift to Detect  | Sample per Variant | Total Sample |
+| --------------- | ------------------ | ------------ |
+| 5% (1% → 1.05%) | 1,500,000          | 3,000,000    |
+| 10% (1% → 1.1%) | 380,000            | 760,000      |
+| 20% (1% → 1.2%) | 97,000             | 194,000      |
+| 50% (1% → 1.5%) | 16,000             | 32,000       |
+| 100% (1% → 2%)  | 4,200              | 8,400        |

 ### Conversion Rate: 3%

-| Lift to Detect | Sample per Variant | Total Sample |
-|----------------|-------------------|--------------|
-| 5% (3% → 3.15%) | 480,000 | 960,000 |
-| 10% (3% → 3.3%) | 120,000 | 240,000 |
-| 20% (3% → 3.6%) | 31,000 | 62,000 |
-| 50% (3% → 4.5%) | 5,200 | 10,400 |
-| 100% (3% → 6%) | 1,400 | 2,800 |
+| Lift to Detect  | Sample per Variant | Total Sample |
+| --------------- | ------------------ | ------------ |
+| 5% (3% → 3.15%) | 480,000            | 960,000      |
+| 10% (3% → 3.3%) | 120,000            | 240,000      |
+| 20% (3% → 3.6%) | 31,000             | 62,000       |
+| 50% (3% → 4.5%) | 5,200              | 10,400       |
+| 100% (3% → 6%)  | 1,400              | 2,800        |

 ### Conversion Rate: 5%

-| Lift to Detect | Sample per Variant | Total Sample |
-|----------------|-------------------|--------------|
-| 5% (5% → 5.25%) | 280,000 | 560,000 |
-| 10% (5% → 5.5%) | 72,000 | 144,000 |
-| 20% (5% → 6%) | 18,000 | 36,000 |
-| 50% (5% → 7.5%) | 3,100 | 6,200 |
-| 100% (5% → 10%) | 810 | 1,620 |
+| Lift to Detect  | Sample per Variant | Total Sample |
+| --------------- | ------------------ | ------------ |
+| 5% (5% → 5.25%) | 280,000            | 560,000      |
+| 10% (5% → 5.5%) | 72,000             | 144,000      |
+| 20% (5% → 6%)   | 18,000             | 36,000       |
+| 50% (5% → 7.5%) | 3,100              | 6,200        |
+| 100% (5% → 10%) | 810                | 1,620        |

 ### Conversion Rate: 10%

-| Lift to Detect | Sample per Variant | Total Sample |
-|----------------|-------------------|--------------|
-| 5% (10% → 10.5%) | 130,000 | 260,000 |
-| 10% (10% → 11%) | 34,000 | 68,000 |
-| 20% (10% → 12%) | 8,700 | 17,400 |
-| 50% (10% → 15%) | 1,500 | 3,000 |
-| 100% (10% → 20%) | 400 | 800 |
+| Lift to Detect   | Sample per Variant | Total Sample |
+| ---------------- | ------------------ | ------------ |
+| 5% (10% → 10.5%) | 130,000            | 260,000      |
+| 10% (10% → 11%)  | 34,000             | 68,000       |
+| 20% (10% → 12%)  | 8,700              | 17,400       |
+| 50% (10% → 15%)  | 1,500              | 3,000        |
+| 100% (10% → 20%) | 400                | 800          |

 ### Conversion Rate: 20%

-| Lift to Detect | Sample per Variant | Total Sample |
-|----------------|-------------------|--------------|
-| 5% (20% → 21%) | 60,000 | 120,000 |
-| 10% (20% → 22%) | 16,000 | 32,000 |
-| 20% (20% → 24%) | 4,000 | 8,000 |
-| 50% (20% → 30%) | 700 | 1,400 |
-| 100% (20% → 40%) | 200 | 400 |
+| Lift to Detect   | Sample per Variant | Total Sample |
+| ---------------- | ------------------ | ------------ |
+| 5% (20% → 21%)   | 60,000             | 120,000      |
+| 10% (20% → 22%)  | 16,000             | 32,000       |
+| 20% (20% → 24%)  | 4,000              | 8,000        |
+| 50% (20% → 30%)  | 700                | 1,400        |
+| 100% (20% → 40%) | 200                | 400          |

 ---

@@ -91,18 +92,21 @@ Duration (days) = (Sample per variant × Number of variants) / (Daily traffic ×
 ### Examples

 **Scenario 1: High-traffic page**
+
 - Need: 10,000 per variant (2 variants = 20,000 total)
 - Daily traffic: 5,000 visitors
 - 100% exposed to test
 - Duration: 20,000 / 5,000 = **4 days**

 **Scenario 2: Medium-traffic page**
+
 - Need: 30,000 per variant (60,000 total)
 - Daily traffic: 2,000 visitors
 - 100% exposed
 - Duration: 60,000 / 2,000 = **30 days**

 **Scenario 3: Low-traffic with partial exposure**
+
 - Need: 15,000 per variant (30,000 total)
 - Daily traffic: 500 visitors
 - 50% exposed to test
@@ -112,6 +116,7 @@ Duration (days) = (Sample per variant × Number of variants) / (Daily traffic ×
 ### Minimum Duration Rules

 Even with sufficient sample size, run tests for at least:
+
 - **1 full week**: To capture day-of-week variation
 - **2 business cycles**: If B2B (weekday vs. weekend patterns)
 - **Through paydays**: If e-commerce (beginning/end of month)
@@ -119,6 +124,7 @@ Even with sufficient sample size, run tests for at least:
 ### Maximum Duration Guidelines

 Avoid running tests longer than 4-8 weeks:
+
 - Novelty effects wear off
 - External factors intervene
 - Opportunity cost of other tests
@@ -131,21 +137,25 @@ Avoid running tests longer than 4-8 weeks:

 **Evan Miller's Calculator**
 https://www.evanmiller.org/ab-testing/sample-size.html
+
 - Simple interface
 - Bookmark-worthy

 **Optimizely's Calculator**
 https://www.optimizely.com/sample-size-calculator/
+
 - Business-friendly language
 - Duration estimates

 **AB Test Guide Calculator**
 https://www.abtestguide.com/calc/
+
 - Includes Bayesian option
 - Multiple test types

 **VWO Duration Calculator**
 https://vwo.com/tools/ab-test-duration-calculator/
+
 - Duration-focused
 - Good for planning

@@ -155,14 +165,15 @@ https://vwo.com/tools/ab-test-duration-calculator/

 With more than 2 variants (A/B/n tests), you need more sample:

-| Variants | Multiplier |
-|----------|------------|
-| 2 (A/B) | 1x |
-| 3 (A/B/C) | ~1.5x |
-| 4 (A/B/C/D) | ~2x |
-| 5+ | Consider reducing variants |
+| Variants    | Multiplier                 |
+| ----------- | -------------------------- |
+| 2 (A/B)     | 1x                         |
+| 3 (A/B/C)   | ~1.5x                      |
+| 4 (A/B/C/D) | ~2x                        |
+| 5+          | Consider reducing variants |

 **Why?** More comparisons increase chance of false positives. You're comparing:
+
 - A vs B
 - A vs C
 - B vs C (sometimes)
@@ -174,22 +185,27 @@ Apply Bonferroni correction or use tools that handle this automatically.
 ## Common Sample Size Mistakes

 ### 1. Underpowered tests
+
 **Problem**: Not enough sample to detect realistic effects
 **Fix**: Be realistic about MDE, get more traffic, or don't test

 ### 2. Overpowered tests
+
 **Problem**: Waiting for sample size when you already have significance
 **Fix**: This is actually fine—you committed to sample size, honor it

 ### 3. Wrong baseline rate
+
 **Problem**: Using wrong conversion rate for calculation
 **Fix**: Use the specific metric and page, not site-wide averages

 ### 4. Ignoring segments
+
 **Problem**: Calculating for full traffic, then analyzing segments
 **Fix**: If you plan segment analysis, calculate sample for smallest segment

 ### 5. Testing too many things
+
 **Problem**: Dividing traffic too many ways
 **Fix**: Prioritize ruthlessly, run fewer concurrent tests

@@ -214,19 +230,23 @@ Options when you can't get enough traffic:
 If you must check results before reaching sample size:

 ### What is it?
+
 Statistical method that adjusts for multiple looks at data.

 ### When to use
+
 - High-risk changes
 - Need to stop bad variants early
 - Time-sensitive decisions

 ### Tools that support it
+
 - Optimizely (Stats Accelerator)
 - VWO (SmartStats)
 - PostHog (Bayesian approach)

 ### Tradeoff
+
 - More flexibility to stop early
 - Slightly larger sample size requirement
 - More complex analysis
@@ -8,6 +8,7 @@ Templates for planning, documenting, and analyzing experiments.
 # A/B Test: [Name]

 ## Overview
+
 - **Owner**: [Name]
 - **Test ID**: [ID in testing tool]
 - **Page/Feature**: [What's being tested]
@@ -23,24 +24,28 @@ We'll know this is true when [metrics].

 ## Test Design

-| Element | Details |
-|---------|---------|
-| Test type | A/B / A/B/n / MVT |
-| Duration | X weeks |
-| Sample size | X per variant |
-| Traffic allocation | 50/50 |
-| Tool | [Tool name] |
-| Implementation | Client-side / Server-side |
+| Element            | Details                   |
+| ------------------ | ------------------------- |
+| Test type          | A/B / A/B/n / MVT         |
+| Duration           | X weeks                   |
+| Sample size        | X per variant             |
+| Traffic allocation | 50/50                     |
+| Tool               | [Tool name]               |
+| Implementation     | Client-side / Server-side |

 ## Variants

 ### Control (A)
+
 [Screenshot]
+
 - Current experience
 - [Key details about current state]

 ### Variant (B)
+
 [Screenshot or mockup]
+
 - [Specific change #1]
 - [Specific change #2]
 - Rationale: [Why we think this will win]
@@ -48,32 +53,38 @@ We'll know this is true when [metrics].
 ## Metrics

 ### Primary
+
 - **Metric**: [metric name]
 - **Definition**: [how it's calculated]
 - **Current baseline**: [X%]
 - **Minimum detectable effect**: [X%]

 ### Secondary
+
 - [Metric 1]: [what it tells us]
 - [Metric 2]: [what it tells us]
 - [Metric 3]: [what it tells us]

 ### Guardrails
+
 - [Metric that shouldn't get worse]
 - [Another safety metric]

 ## Segment Analysis Plan
+
 - Mobile vs. desktop
 - New vs. returning visitors
 - Traffic source
 - [Other relevant segments]

 ## Success Criteria
+
 - Winner: [Primary metric improves by X% with 95% confidence]
 - Loser: [Primary metric decreases significantly]
 - Inconclusive: [What we'll do if no significant result]

 ## Pre-Launch Checklist
+
 - [ ] Hypothesis documented and reviewed
 - [ ] Primary metric defined and trackable
 - [ ] Sample size calculated
@@ -93,46 +104,50 @@ We'll know this is true when [metrics].
 # A/B Test Results: [Name]

 ## Summary
-| Element | Value |
-|---------|-------|
-| Test ID | [ID] |
-| Dates | [Start] - [End] |
-| Duration | X days |
-| Result | Winner / Loser / Inconclusive |
-| Decision | [What we're doing] |
+
+| Element  | Value                         |
+| -------- | ----------------------------- |
+| Test ID  | [ID]                          |
+| Dates    | [Start] - [End]               |
+| Duration | X days                        |
+| Result   | Winner / Loser / Inconclusive |
+| Decision | [What we're doing]            |

 ## Hypothesis (Reminder)
+
 [Copy from test plan]

 ## Results

 ### Sample Size
+
 | Variant | Target | Actual | % of target |
-|---------|--------|--------|-------------|
-| Control | X | Y | Z% |
-| Variant | X | Y | Z% |
+| ------- | ------ | ------ | ----------- |
+| Control | X      | Y      | Z%          |
+| Variant | X      | Y      | Z%          |

 ### Primary Metric: [Metric Name]
-| Variant | Value | 95% CI | vs. Control |
-|---------|-------|--------|-------------|
-| Control | X% | [X%, Y%] | — |
-| Variant | X% | [X%, Y%] | +X% |
+
+| Variant | Value | 95% CI   | vs. Control |
+| ------- | ----- | -------- | ----------- |
+| Control | X%    | [X%, Y%] | —           |
+| Variant | X%    | [X%, Y%] | +X%         |

 **Statistical significance**: p = X.XX (95% = sig / not sig)
 **Practical significance**: [Is this lift meaningful for the business?]

 ### Secondary Metrics

-| Metric | Control | Variant | Change | Significant? |
-|--------|---------|---------|--------|--------------|
-| [Metric 1] | X | Y | +Z% | Yes/No |
-| [Metric 2] | X | Y | +Z% | Yes/No |
+| Metric     | Control | Variant | Change | Significant? |
+| ---------- | ------- | ------- | ------ | ------------ |
+| [Metric 1] | X       | Y       | +Z%    | Yes/No       |
+| [Metric 2] | X       | Y       | +Z%    | Yes/No       |

 ### Guardrail Metrics

-| Metric | Control | Variant | Change | Concern? |
-|--------|---------|---------|--------|----------|
-| [Metric 1] | X | Y | +Z% | Yes/No |
+| Metric     | Control | Variant | Change | Concern? |
+| ---------- | ------- | ------- | ------ | -------- |
+| [Metric 1] | X       | Y       | +Z%    | Yes/No   |

 ### Segment Analysis

@@ -151,12 +166,15 @@ We'll know this is true when [metrics].
 ## Interpretation

 ### What happened?
+
 [Explanation of results in plain language]

 ### Why do we think this happened?
+
 [Analysis and reasoning]

 ### Caveats
+
 [Any limitations, external factors, or concerns]

 ## Decision
@@ -170,14 +188,17 @@ We'll know this is true when [metrics].
 ## Learnings

 ### What we learned
+
 - [Key insight 1]
 - [Key insight 2]

 ### What to test next
+
 - [Follow-up test idea 1]
 - [Follow-up test idea 2]

 ### Impact
+
 - **Projected lift**: [X% improvement in Y metric]
 - **Business impact**: [Revenue, conversions, etc.]
 ```
@@ -189,11 +210,11 @@ We'll know this is true when [metrics].
 For tracking all tests in a central location:

 ```markdown
-| Test ID | Name | Page | Dates | Primary Metric | Result | Lift | Link |
-|---------|------|------|-------|----------------|--------|------|------|
-| 001 | Hero headline test | Homepage | 1/1-1/15 | CTR | Winner | +12% | [Link] |
-| 002 | Pricing table layout | Pricing | 1/10-1/31 | Plan selection | Loser | -5% | [Link] |
-| 003 | Signup form fields | Signup | 2/1-2/14 | Completion | Inconclusive | +2% | [Link] |
+| Test ID | Name                 | Page     | Dates     | Primary Metric | Result       | Lift | Link   |
+| ------- | -------------------- | -------- | --------- | -------------- | ------------ | ---- | ------ |
+| 001     | Hero headline test   | Homepage | 1/1-1/15  | CTR            | Winner       | +12% | [Link] |
+| 002     | Pricing table layout | Pricing  | 1/10-1/31 | Plan selection | Loser        | -5%  | [Link] |
+| 003     | Signup form fields   | Signup   | 2/1-2/14  | Completion     | Inconclusive | +2%  | [Link] |
 ```

 ---
@@ -225,15 +246,18 @@ For simple tests that don't need full documentation:
 **Current sample**: X% of target

 ### Preliminary observations
+
 [What we're seeing - without making decisions yet]

 ### Next steps
+
 [What happens next]

 ### Timeline
+
 - [Date]: Analysis complete
 - [Date]: Decision and recommendation
- [Date]: Implementation (if winner)
+- [Date]: Implementation 'if winner'
 ```

 ---
@@ -242,14 +266,14 @@ For simple tests that don't need full documentation:

 For deciding which tests to run:

-| Factor | Weight | Test A | Test B | Test C |
-|--------|--------|--------|--------|--------|
-| Potential impact | 30% | | | |
-| Confidence in hypothesis | 25% | | | |
-| Ease of implementation | 20% | | | |
-| Risk if wrong | 15% | | | |
-| Strategic alignment | 10% | | | |
-| **Total** | | | | |
+| Factor                   | Weight | Test A | Test B | Test C |
+| ------------------------ | ------ | ------ | ------ | ------ |
+| Potential impact         | 30%    |        |        |        |
+| Confidence in hypothesis | 25%    |        |        |        |
+| Ease of implementation   | 20%    |        |        |        |
+| Risk if wrong            | 15%    |        |        |        |
+| Strategic alignment      | 10%    |        |        |        |
+| **Total**                |        |        |        |        |

 Scoring: 1-5 (5 = best)

@@ -260,9 +284,9 @@ Scoring: 1-5 (5 = best)
 For collecting test ideas:

 ```markdown
-| ID | Page/Area | Observation | Hypothesis | Potential Impact | Status |
-|----|-----------|-------------|------------|------------------|--------|
-| H1 | Homepage | Low scroll depth | Shorter hero will increase scroll | High | Testing |
-| H2 | Pricing | Users compare plans | Comparison table will help | Medium | Backlog |
-| H3 | Signup | Drop-off at email | Social login will increase completion | Medium | Backlog |
+| ID  | Page/Area | Observation         | Hypothesis                            | Potential Impact | Status  |
+| --- | --------- | ------------------- | ------------------------------------- | ---------------- | ------- |
+| H1  | Homepage  | Low scroll depth    | Shorter hero will increase scroll     | High             | Testing |
+| H2  | Pricing   | Users compare plans | Comparison table will help            | Medium           | Backlog |
+| H3  | Signup    | Drop-off at email   | Social login will increase completion | Medium           | Backlog |
 ```