◆ AI Learning · Hard rule

What are common thinking errors when working with data?

A developer must understand common thinking errors. These errors can affect how data is understood. Learning these errors helps a developer make better decisions.

By Pawan Nayar · from the LLOS AI Learning record

The rule

A developer must understand common thinking errors.

These errors can affect how data is understood. Learning these errors helps a developer make better decisions.

Data sufficiency asks if there is enough information to answer a question. Base rate neglect means ignoring general probabilities. Proxy metrics are measurements used when the real thing cannot be measured directly. Survivorship bias happens when only successful examples are studied. Simpson's Paradox shows how trends can disappear or reverse when data groups are combined. The Y-axis trap is when graphs mislead by changing the scale. Relative versus absolute risk explains how 'double the risk' can still be a very small risk. Systems thinking looks at how parts of a system affect each other. Trade-offs are choices made when resources are limited.

A developer is building a system to predict customer churn. The developer only looks at data from customers who have already left. This is survivorship bias. The system might not learn why other customers stay. The prediction will be less accurate.

Words you'll need

Resolved from this lesson's own record — real definitions, not invented for this page.

Data Interpretation

Making sense of numbers and patterns in data

Model Interpretability

Understanding how AI makes its decisions

Causal Inference

Understanding cause-and-effect relationships in data

Overfitting

The AI memorized instead of learning the pattern

Questions people ask

More from the same record, pulled by shared topic — not written new for this page.

What is the difference between an incident and a pattern?

An incident is a one-time mistake or problem. It happens only once.

When should an AI model fix a problem without explaining its steps or offering choices?

If a problem is clearly wrong, the AI model should fix it immediately. This includes bugs, bad design, or known poor practices.

How should a developer present different options for a project?

When a developer offers choices for a project, the developer must explain what each choice includes. This means describing what is new, what is reused, and what is uncertain.

How can a person understand their feelings when they say 'no' or feel tired from social demands?

Feeling guilty when saying 'no' is a signal. This signal helps a person avoid resentment later.