Category: Blog
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The Tutor Handbook Vol No.0144 | The Causal-Attribution Gap — How a Tutor Reports Improvement After an Intervention Without Claiming the Intervention Caused More Than the Evidence Can Establish
THB-0144 — How tutors distinguish observed improvement from evidence that tutoring caused the improvement when school teaching, practice, maturation and other changes may also contribute.
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The Tutor Handbook Vol No.0143 | The Regression-to-the-Mean Trap — How a Tutor Interprets a Rebound After an Unusually Bad Result Without Mistaking Statistical Return for Proof the Intervention Worked
THB-0143 — How tutors interpret improvement after an unusually low result without automatically attributing the rebound to the latest intervention.
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The Tutor Handbook Vol No.0142 | The Correlated-Evidence Trap — How a Tutor Stops Several Similar Successes From Pretending to Be Independent Confirmation of Learning
THB-0142 — How tutors detect when several apparent confirmations share the same task family, support, source, prior exposure or scoring process and therefore provide less independent evidence than they appear to.
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The Tutor Handbook Companion 0141A | The Error-Cost Decision Matrix — Set Evidence Thresholds for Reversible and Sticky Decisions
Companion to canonical Vol.0141, The Error-Cost Asymmetry Gate. This page applies the mechanism as a decision matrix for reversible, costly, sticky and identity-shaping tutoring actions.
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The Tutor Handbook Vol No.0141 | The Error-Cost Asymmetry Gate — How a Tutor Changes the Evidence Burden When Acting Too Early and Acting Too Late Do Not Harm the Learner Equally
THB-0141 — How tutors adjust evidence requirements when false alarms, missed problems, premature support removal and delayed intervention carry unequal educational costs.
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The Tutor Handbook Vol No.0140 | The Precommitted Route-Change Threshold — How a Tutor Decides Before the Next Check What Evidence Will Trigger, Hold or Reverse a Learning-Route Change
THB-0140 — How tutors precommit the evidence conditions that will trigger, hold or reverse a learning-route change so the decision rule does not drift after the result is known.
