Mom Test

# Avoiding Bad Data: Compliments, Fluff, and Ideas

Bad data is worse than no data because it gives you false confidence. You build the wrong thing, launch to crickets, and can't figure out what went wrong because "everyone said they loved it." This reference covers the three types of bad data, how to recognize them in real-time, and specific techniques for deflecting each type back to useful information.

## The Three Types of Bad Data

### Type 1: Compliments

Compliments are the most common and most dangerous form of bad data. They feel amazing in the moment and are completely worthless for decision-making.

**What compliments sound like:**
- "That's a really cool idea!"
- "I can definitely see myself using that."
- "You guys are going to crush it."
- "This is exactly what the market needs."
- "I love it. When can I get it?"
- "You're solving a real problem."
- "My team would love this."

**Why compliments are dangerous:**
Compliments contain zero information about whether someone will change their behavior, pay money, or use your product. They're a social reflex -- the same way you say "I'm good" when someone asks how you are. People compliment you because:
- They want to be supportive
- Saying "bad idea" feels cruel
- They haven't thought about it deeply enough to have a real opinion
- They want the conversation to end pleasantly

**How to deflect compliments:**

| Compliment | Deflection |
|-----------|------------|
| "That's a great idea!" | "Thanks! But tell me -- how are you dealing with this problem right now?" |
| "I'd definitely use that" | "That's encouraging. What are you currently using? What's frustrating about it?" |
| "My team would love this" | "What's your team struggling with specifically? Walk me through a recent example." |
| "You're going to crush it" | "I appreciate that. To make sure we build the right thing -- what's the biggest pain point in your workflow today?" |
| "This is exactly what we need" | "What have you tried before? Why didn't those solutions work?" |

**The golden rule:** Every time you receive a compliment, convert it into a question about their life and past behavior.

### Type 2: Fluff

Fluff is vague, generic, or hypothetical talk that sounds informative but carries no real-world weight. It's the verbal equivalent of empty calories.

**Three sub-types of fluff:**

#### Generic Claims
- "I usually..." (without a specific instance)
- "I always..." (really? always?)
- "I never..." (provably false in most cases)
- "We generally..." (who, specifically? when?)

**Deflection:** "Can you give me a specific example of the last time that happened?"

#### Hypothetical Promises
- "I would definitely..." (but you haven't)
- "If you built that, I'd..." (future tense = fiction)
- "I think I'd probably..." (double hedging)
- "That would be worth at least $X to me" (imaginary money)

**Deflection:** "You mentioned you'd pay $X. What's the most you've actually paid for a tool like this?"

#### Future-Tense Predictions
- "Next quarter, we're planning to..."
- "We'll probably need something like this soon"
- "I'm going to start looking for a solution"
- "That's on our roadmap for later this year"

**Deflection:** "When did you first realize this was a problem? What have you done about it so far?"

**The fluff test:** If you can't assign a date, a dollar amount, or a specific event to what they said, it's fluff. Dig until you hit concrete ground.

### Type 3: Ideas (Unsolicited Feature Requests)

When people understand what you're building, they start designing your product for you. This feels collaborative and exciting, but it's usually misleading because they're solving an imagined version of the problem.

**What idea-giving sounds like:**
- "You should totally add [feature]"
- "It would be amazing if it could also [function]"
- "Have you thought about integrating with [tool]?"
- "What if it also did [tangentially related thing]?"
- "The real killer feature would be [thing they thought of in the last