Guide-linked quiz · Medium Level
Cohen's Kappa Quiz
Explore observed and expected agreement, weighted kappa and annotation reliability through worked examples of categorical labels.
Question 1 of 8
Two annotators have observed agreement of 0.8 and expected agreement of 0.5. Which kappa value follows?
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- Two annotators have observed agreement of 0.8 and expected agreement of 0.5. Which kappa value follows?
- 0.3
- 0.4
- 0.8
- 0.6
- One annotator uses a category 60% of the time and the other uses it 50% of the time. How much does that category contribute to expected agreement?
- 0.10
- 0.30
- 0.55
- 1.10
- Why does the expected-agreement term in kappa not establish that annotators were guessing randomly?
- It is calculated from the number of disagreements alone
- It is identical to the observed agreement for every dataset
- It is a reference derived from label frequencies, not an observation of their reasoning
- It assumes every category must have equal frequency
- For low, medium and high incident severity, why might a team choose weighted kappa?
- To give different penalties to one-level and two-level disagreements
- To remove all disagreements from the calculation
- To guarantee a higher reliability score
- To avoid specifying a category order
- Both annotators assign every case to the same single category. Why should ordinary kappa not be reported as a straightforward reliability success?
- Perfect raw agreement automatically makes kappa one
- The score ignores the common category and evaluates only disagreements
- Subtracting expected agreement alone gives a complete kappa calculation
- Expected agreement is one, leaving a zero denominator
- A second reviewer copies the first reviewer's labels and achieves perfect agreement. Which conclusion is justified?
- The labels have been independently validated
- The agreement does not establish independent reliability
- Every label is necessarily correct
- The dataset can be used without checking instructions
- Which accompanying information helps interpret a kappa score when category frequencies are uneven?
- Only the raw agreement percentage, without category counts
- Only whether kappa exceeds a universal cutoff
- Sample size, raw agreement and the agreement table
- Only the weighted score, without specifying category order
- Two annotators both follow a mistaken rule and agree strongly. What does their high kappa fail to establish?
- That their labels are factually correct
- That their observed agreement exceeds its frequency-based expectation
- That they used the same labels on many cases
- That their assignments show consistency under the shared rule
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