Tables Of Investigation

Tables of Investigation

Apply Francis Bacon's systematic inductive method to investigate causes. Constructs Tables of Presence, Absence, and Degrees to methodically eliminate false causes and harvest a tentative hypothesis grounded in evidence.

When to Use

• User asks "Why does X happen?" or "What causes Y?"
• Need to understand root cause of a recurring problem
• Pattern exists but underlying mechanism is unclear
• Multiple competing explanations exist with no clear way to choose
• Previous causal theories have failed to explain observations
• Request to "investigate systematically" or "find the real cause"
• Moving from "we notice X" to "we understand X"

Inputs

The Three Tables

Table of Presence (Tabula Praesentiae)

Collect all instances where the phenomenon occurs. Note the circumstances surrounding each instance.

Purpose: Identify what factors are consistently present when the effect appears.

Questions:

• When has this happened before?
• What circumstances were present in each case?
• What do all positive instances have in common?

Table of Absence (Tabula Absentiae)

Collect instances where the phenomenon is absent despite similar conditions. Focus on cases that closely resemble positive instances but lack the effect.

Purpose: Identify factors that can be ruled out as causes (present in both positive and negative cases).

Questions:

• When did we expect this to happen but it didn't?
• What similar situations produced different outcomes?
• What factors are present in failures but absent in successes (or vice versa)?

Table of Degrees (Tabula Graduum)

Note how the phenomenon varies in intensity. Correlate changes in the effect with changes in circumstances.

Purpose: Identify factors that vary together with the phenomenon - when one increases, the other increases (or decreases).

Questions:

• When is this effect stronger or weaker?
• What varies together with the effect?
• Are there threshold effects or proportional relationships?

Workflow

Phase 1: Define the Phenomenon Precisely

State exactly what is being investigated. Avoid vague language.

Bad: "Why do some projects fail?" Good: "Why do projects initiated in Q4 with remote teams exceed budget by more than 20%?"

Phase 2: Construct the Table of Presence

List every known instance where the phenomenon occurs:

Identify common factors - What appears in multiple positive instances?

Phase 3: Construct the Table of Absence

List instances where the phenomenon was expected but did not occur:

Apply exclusion - Any factor present in BOTH Table of Presence and Table of Absence cannot be the cause.

Phase 4: Construct the Table of Degrees

Note variations in the phenomenon's intensity:

Identify correlations - What varies together with the effect?

Phase 5: Apply Exclusion

Systematically eliminate false causes:

Step 1: Strike factors present in both success and failure (Table of Presence vs. Table of Absence)

Step 2: Strike factors that don't vary with the effect (Table of Degrees)

Step 3: What remains is the First Harvest

Phase 6: State the First Harvest

The First Harvest is a tentative hypothesis, not a conclusion. It is:

• What remains after exclusion
• Supported by the evidence but not proven
• Subject to further testing through crucial instances

Output Format

## Tables of Investigation: [Phenomenon]

### Phenomenon Under Investigation
[Precise statement of what is being studied]

---

### Table of Presence

| # | Instance | Circumstances | Notable Factors |
|---|----------|--------------|-----------------|
| 1 | [Case] | [Context] | [Factors present] |
| 2 | [Case] | [Context] | [Factors present] |
| 3 | [Case] | [Context] | [Factors present] |

**Common Factors Identified:**
- [Factor A] - present in X of Y instances
- [Factor B] - present in X of Y instances

---

### Table of Absence

| # | Instance | Expected Because | What Differed |
|---|----------|-----------------|---------------|
| 1 | [Case] | [Similarity to positive] | [Key difference] |
| 2 | [Case] | [Similarity to positive] | [Key difference] |

**Factors Eliminated by Absence:**
- ~~[Factor C]~~ - present in negative instances; cannot be the cause
- ~~[Factor D]~~ - present in negative instances; cannot be the cause

---

### Table of Degrees

| Instance | Phenomenon Level | Potential Cause Level | Correlation |
|----------|-----------------|----------------------|-------------|
| [Case] | [Low/Med/High] | [Low/Med/High] | [+/-/0] |

**Correlations Identified:**
- [Factor E] varies together with phenomenon (positive correlation)
- [Factor F] shows no relationship (eliminated)

---

### Exclusion Process

**Eliminated by Table of Absence:**
1. ~~[Factor]~~ - Reason
2. ~~[Factor]~~ - Reason

**Eliminated by Table of Degrees:**
1. ~~[Factor]~~ - Does not vary with effect
2. ~~[Factor]~~ - Inverse of expected relationship

**Remaining After Exclusion:**
- [Factor X]
- [Factor Y]

---

### First Harvest (Tentative Hypothesis)

**Hypothesis:** [Statement of what the evidence suggests causes the phenomenon]

**Supporting Evidence:**
- Present in all positive instances
- Absent in negative instances
- Varies together with the effect

**Confidence Level:** [Low/Medium/High]
- [Rationale for confidence assessment]

**Required for Confirmation:**
- [Crucial instance that would confirm or refute]
- [Additional data needed]

---

*"Truth is the daughter of time, not of authority." - Francis Bacon*

Constraints

• Collect instances before forming hypotheses (avoid anticipations of nature)
• The First Harvest is tentative, not proven
• Absence of evidence in Table of Absence is not evidence of absence - actively seek counter-instances
• The method reveals correlation; causal mechanism requires further investigation
• Be honest about gaps in the tables
• Include known counter-instances even if inconvenient

Outputs

Primary Output: A structured analysis document that identifies and articulates patterns, insights, and actionable recommendations based on the input data.

Format:

## Analysis: [Topic]

### Key Findings
- [Finding 1]
- [Finding 2]
- [Finding 3]

### Recommendations
1. [Action 1]
2. [Action 2]
3. [Action 3]

Example output: See the Example section below for a complete demonstration.

Example

Input: "Why do some of our sales calls convert and others don't?"

Output:

Why this works:

This example demonstrates the key principles of the skill in action. The approach is effective because:

• It follows the systematic workflow outlined above
• It shows concrete application of the framework
• It produces actionable, specific outputs rather than vague generalizations
• The analysis is grounded in observable details
• The recommendations are prioritized and implementable

Alternative applications:

This same approach can be applied to:

• Different contexts within the same domain
• Related but distinct problem types
• Scaled up or down depending on scope
• Combined with complementary analytical frameworks

Tables of Investigation: Sales Call Conversion

Phenomenon Under Investigation

Sales calls that result in closed deals (conversion) versus calls that do not convert despite reaching decision-maker.

Table of Presence (Converted Calls)

Common Factors Identified:

• Demo within first 10 minutes - present in 4/4 instances
• Specific use case discussed - present in 4/4 instances
• ROI/financial impact mentioned - present in 2/4 instances
• Referral source - present in 1/4 instances

Table of Absence (Non-Converting Calls)

Factors Eliminated by Absence:

• Decision-maker presence - present in non-converting calls
• Warm lead/referral status - Epsilon was warm, Zeta was referred
• Budget availability - Eta had confirmed budget

Table of Degrees

Correlations Identified:

• Earlier demo timing correlates with conversion
• Higher use case specificity correlates with conversion

Exclusion Process

Eliminated by Table of Absence:

1. Decision-maker presence - Present in both converting and non-converting
2. Lead warmth/referral - Present in non-converting calls
3. Budget confirmation - Present in Eta (non-converting)

Eliminated by Table of Degrees:

1. Call duration - No consistent relationship
2. Time of day - No pattern observed

Remaining After Exclusion:

• Demo within first 10-15 minutes
• Specific use case discussion (not generic features)

First Harvest (Tentative Hypothesis)

Hypothesis: Conversion depends on (1) demonstrating the product within the first 10-15 minutes AND (2) connecting the demo to the prospect's specific use case rather than generic features.

Supporting Evidence:

• All conversions had early demos with specific use cases
• All non-conversions lacked one or both factors
• Degree of specificity correlates with conversion speed

Confidence Level: Medium

• Sample size is small (7 calls)
• Correlation established but mechanism unclear
• Other factors may be confounded with demo timing

Required for Confirmation:

• Crucial instance: Call with late demo but highly specific use case
• Crucial instance: Call with early demo but generic pitch
• Larger sample across different sales reps

"Truth is the daughter of time, not of authority." - Francis Bacon

Integration

This skill is part of the Francis Bacon expert persona. Use it to investigate causes systematically. It pairs well with:

• idol-diagnosis for clearing cognitive obstacles before investigation
• anticipation-vs-interpretation for evaluating the First Harvest
• light-vs-fruit-classification for determining if this is the right type of investigation