Error Analysis

How does the idea of “Error Analysis” change the decision we make?

This is Applied AI through a practical look at Error Analysis, with attention to evidence, trade-offs, and uncertainty. Connect Error Analysis to evidence someone else can check. A confident claim is not enough when its source and limits are hidden. After this lesson, you can assess an explanation of Error Analysis by checking its source, evidence, and unknowns.

After this lesson

• This is Applied AI through a practical look at Error Analysis, with attention to evidence, trade-offs, and uncertainty.
• Use the idea of “Error Analysis” to interpret one realistic situation.
• Explain the limits of the concept and the information that still needs to be checked.

Start with the situation

Understand the situation first. The label can come later.

A high average score can hide serious failures for one language or user group. This lesson uses the idea of “Error Analysis” to examine that situation without treating a single term as the answer to every problem.

This is Applied AI through a practical look at Error Analysis, with attention to evidence, trade-offs, and uncertainty. Prepare reference examples, a rubric, a baseline, and error analysis before wider use. Connect the term to a decision someone genuinely needs to make.

See how the decision unfolds

Move from the situation to a choice others can review.

Do not rush the choice

Two ways to look at Error Analysis

The stronger choice is the one whose evidence, owner, and limits can be explained, not simply the more sophisticated option.

Visual model

Map the parts before choosing what to do.

Read the diagram as a map of Error Analysis: begin with the context, follow the connections, and inspect the highlighted point before making a decision.

Pause for a moment

What evidence could change this decision?

Answer before opening the discussion. Name one fact and one assumption.

A tempting shortcut

A familiar term can still lead us to the wrong decision.

Why this can seem reasonable

One successful demo does not prove that a workflow is reliable. This mistake often appears when a label is used before the problem is understood. Write down your assumptions so another person can review them.

How to check it

Connect Error Analysis to evidence someone else can check. A confident claim is not enough when its source and limits are hidden. Begin with what can be observed, then separate facts, assumptions, and open questions.

Try it on your work

Try it with one small piece of real work.

1. Choose one real situation related to Error Analysis.
2. Separate what you can observe from what you are assuming.
3. Write one decision, its owner, and the evidence needed to review it.
4. Name the signal that would make you stop or change direction.

Choose a task you know. List what is known, what is still an assumption, and what must be tested before using the idea of “Error Analysis”. The larger module activity is: Test ten examples and group the errors you find. Keep the first version small enough for another person to review in a few minutes.

Quick practice

Choose a task you know. List what is known, what is still an assumption, and what must be tested before using the idea of “Error Analysis”. The larger module activity is: Test ten examples and group the errors you find.

Summary

• This is Applied AI through a practical look at Error Analysis, with attention to evidence, trade-offs, and uncertainty.
• Use examples and evidence to test your understanding.
• Record the limits, risks, and conditions that should trigger another review.

Continue from here

• Cost and Latency: Continue the idea from Testing Quality with a closely related example.
• Confidence and Feedback: Connect this lesson to Product Thinking and test the idea in another context.
• Versioning: See how the same decision changes when viewed through Prompting.

Sources and further reading

• Evaluation best practices: OpenAI · official-documentation. Primary reference for the definition, evidence, or limits discussed in “Error Analysis”.
• Classification: Accuracy, recall, precision, and related metrics: Google for Developers · official-course. Further evidence and context for checking the explanation in “Error Analysis”.
• Model evaluation: scikit-learn · official-documentation. Further evidence and context for checking the explanation in “Error Analysis”.