# ⚖️ Facts vs Preferences: What Counts as Evidence

By Better Wise

## Where this sits: sorting what you already collected

In the last lesson you named the decision, ranked your goals, and wrote success criteria you could actually test. That gave you a container. This lesson fills it with inputs, and the key move is refusing to treat every input the same. A cost figure and a strong opinion are not the same kind of thing, and mixing them is where most decision documents quietly fall apart.

By the end of this lesson you can:

- Sort an input into one of three buckets: fact, preference, or assumption
- Rate any source on reliability and relevance, as two separate questions
- Spot a preference wearing a fact costume
- Turn your riskiest assumption into a cheap test with a deadline

## Three buckets, three different jobs

Every input on your decision table belongs in exactly one bucket.

| Bucket | What it is | Litmus test | Example |
| --- | --- | --- | --- |
| **Fact** | A claim an independent check can confirm or refute | Who else could verify this, and how? | "The current tool costs $240 per seat per year." |
| **Preference** | A value, taste, or priority held by a person | Whose preference is this, and can it be ranked? | "I would rather not switch tools mid-quarter." |
| **Assumption** | A claim you are treating as true without having checked it | What would I test, and when? | "The team will adopt a new tool within two weeks." |

Facts reduce uncertainty. Preferences decide what you are optimizing for. Assumptions are the debt you are carrying until you check them. None is illegitimate, but each needs a different response.

```mermaid
flowchart TD
  A["Input on the decision table"] --> B{"Can an independent check prove it wrong?"}
  B -- Yes --> F["📊<br/>Fact"]
  B -- No --> C{"Does it express what someone values or wants?"}
  C -- Yes --> P["❤️<br/>Preference"]
  C -- No --> S["❓<br/>Assumption"]
```

The most common failure is a preference in a fact costume. "The team needs better reporting" sounds like a finding. Usually it is a want, and the honest version is "Priya wants reporting that does not require a manual export." One version can be checked. The other can only be ranked against other wants.

Note what a fact is not. _____. Popularity is not a check. Confidence is not a check. A fact that cannot be traced to a source and a date is really an assumption.

### Exercise 1: Sort six inputs from the project thread

Your project is a decision brief on whether to move your team's reporting from spreadsheets to a paid BI tool. Sort each input and say what you would do with it next.

1. Finance: "Current licence cost is $240 per seat per year, 14 seats."
2. A colleague: "Spreadsheets are fine, people just need to be more careful."
3. Your own note: "Most of the team will need less than a day of training."
4. Vendor case study: "Teams like ours cut reporting time by 40 percent."
5. Your manager: "Whatever we pick, it has to be live before the Q3 board pack."
6. Team survey: "9 of 12 respondents said exports break at least once a month."

**Answer guidance for Exercise 1**

1. **Fact.** Verifiable with the contract. Date it and keep it as anchor evidence.
2. **Preference** (and a vague one). Ask for the owner and the specific preference: which part of the current workflow does this person want protected?
3. **Assumption.** Load-bearing if training hours could kill the timeline. Test it with a 30-minute pilot on one real report.
4. **Fact about someone else, relevant only partly.** Real, but the context may differ. Treat it as a weak signal until you find your own comparable case.
5. **Constraint, effectively a fact about the deadline.** It is not up for debate, so it moves to your constraints list, not your evidence list.
6. **Fact, with a caveat.** Self-reported and from a small sample, so note the sample size alongside the number.

Notice how many "facts" arrived with a caveat. That is normal. Sorting is about the type of claim, not about pretending you have perfect data.

## Reliability and relevance are two separate questions

Two inputs can both be true and still deserve wildly different weight. Score them on two axes.

- _____ asks whether this source tends to be right when the answer can be checked. Track record, incentives, closeness to the primary data.
- _____ asks whether it speaks to this decision, in this context, at this time.

| | **High relevance** | **Low relevance** |
| --- | --- | --- |
| **High reliability** | Anchor evidence. Build the case on it. | Useful context, but do not let it drive the choice. |
| **Low reliability** | A lead to test, never proof. | Drop it. |

Two traps live in this grid. A vivid anecdote from a colleague sits low on reliability and high on relevance, which makes it feel like evidence. A confident expert from a very different company sits high on reliability and low on relevance, which makes it feel authoritative. Neither should settle the decision.

### Exercise 2: Score sources, then pay down one assumption

For input 4 (the vendor case study) and input 6 (the team survey), write one line each on reliability, one line on relevance, and a verdict: anchor, context, lead, or drop. Then take input 3 and convert it into a test with a deadline.

**Answer guidance for Exercise 2**

**Input 4, vendor case study.** Reliability: moderate, the vendor benefits from a good number. Relevance: moderate to low, their baseline may not match yours. Verdict: **lead**. Ask the vendor for the raw baseline, or find a comparable team you can question directly.

**Input 6, team survey.** Reliability: moderate, self-reported and small sample, but it measures your own team. Relevance: high, it is your workflow. Verdict: **anchor**, with the caveat written next to the number.

**Input 3, the training assumption.** Test: one team member rebuilds a real monthly report in the tool while you time it. Deadline: end of this week, before the vendor demo. If it takes over two hours, the assumption is false and the timeline goal needs revisiting.

A load-bearing assumption is one where _____. Test those first. Assumptions that would not change your choice can wait.

## Rules of thumb for keeping evidence honest

- If you cannot name the check, it is not a fact yet. "Everyone knows" is not a check.
- Write preferences with an owner attached: "Priya prefers X" beats "X is needed."
- Before you gather more evidence, write down what would change your mind. Otherwise you will collect confirmation and call it research.
- One strong, close, dated source beats five weak ones. Do not average anecdotes.
- Date every fact. A fact with no date drifts into an assumption.
- When reliability and relevance disagree, ask which one this decision actually turns on.
- Keep a visible assumption list with an owner and a test date. Untested assumptions are how good briefs become bad decisions.

## Wrap-up

- Every input is a fact, a preference, or an assumption, and each needs a different response.
- Reliability and relevance are separate scores. High on one does not rescue low on the other.
- Test load-bearing assumptions first, cheaply, with a deadline.
- A decision brief gets stronger when the assumptions are visible, not when they are removed.

Next up is the checkpoint, where you will frame a decision and sort its inputs end to end, using exactly the buckets and scoring you just practiced.

Front: What makes a claim a fact?
Back: An independent check can confirm or refute it, and you can name that check.

Front: What is a preference?
Back: A value, taste, or priority held by a person that can be ranked but not proven true or false.

Front: What is an assumption?
Back: A claim you are treating as true without having checked it yet.

Front: What is the quickest triage question for a fact?
Back: Can an independent check prove this wrong, and who would run it?

Front: How do you spot a preference disguised as a fact?
Back: It states a want as a property of the world, such as 'the team needs better reporting'.

Front: What does reliability measure?
Back: Whether the source tends to be right, based on track record, incentives, and closeness to the data.

Front: What does relevance measure?
Back: Whether the source speaks to this decision, in this context, at this time.

Front: How should you treat a vivid anecdote from a colleague?
Back: Low reliability, high relevance: a lead to test, not proof.

Front: How should you treat a confident expert from a very different company?
Back: High reliability, low relevance: useful context, not the deciding input.

Front: What makes an assumption load-bearing?
Back: If it were false, the decision would flip.

Front: Why should every fact carry a date?
Back: An undated fact drifts into an assumption as conditions change.

Front: What should you write before gathering more evidence?
Back: What would change your mind, so you do not just collect confirmation.

Front: What do the four cells of the reliability-relevance grid recommend?
Back: Anchor, context, lead to test, or drop.

---

## Contents

Part of [🧭 Decision-Making and Autonomy: Trust Your Judgment in the Age of AI](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai.md)

- **🧩 Frame the Decision and Set the Bar**
  - [🔍 Name the Real Decision with Problem Framing](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/name-the-real-decision-with-problem-framing.md)
  - [🎯 Goals, Constraints, and Success Criteria](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/goals-constraints-and-success-criteria.md) (sign-in required)
  - [⚖️ Facts vs Preferences: What Counts as Evidence](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/facts-vs-preferences-what-counts-as-evidence.md) — this lesson
  - [✅ Checkpoint: Framing and Evidence Sorting](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/checkpoint-framing-and-evidence-sorting.md)
- **🧪 Compare and Stress-Test the Options**
  - [🔀 Opportunity Cost and Side-by-Side Comparison](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/opportunity-cost-and-side-by-side-comparison.md)
  - [🚨 Cognitive Biases and the Pre-Mortem](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/cognitive-biases-and-the-pre-mortem.md)
  - [⏱️ Reversibility, Decision Speed, and Calibrated Confidence](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/reversibility-decision-speed-and-calibrated-confidence.md)
  - [✅ Checkpoint: Choosing Under Uncertainty](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/checkpoint-choosing-under-uncertainty.md)
- **🔁 Use AI, Decide, and Review the Outcome**
  - [🤖 AI as a Question Generator, Not an Authority](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/ai-as-question-generator-not-an-authority.md)
  - [📋 Post-Decision Reviews: Process Over Outcome](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/post-decision-reviews-process-over-outcome.md)
  - [🛠️ Project: Build a Decision Brief with Pre-Mortem and Review Plan](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/project-build-decision-brief-with-premortem-and-review-plan.md) (premium content)
  - [✅ Checkpoint: AI Use and Decision Review](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/checkpoint-ai-use-and-decision-review.md) (premium content)
- [Course cheatsheet](https://everwise.study/courses/decision-making-and-autonomy-trust-your-judgment-in-the-age-of-ai/cheatsheet.md) (premium content)