Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Means
▶︎ Watch it animatedinteractive step-through · ~3 min · optionalAn interval for $\mu_1 - \mu_2$ is read against 0. Entirely above gives evidence that $\mu_1 > \mu_2$, entirely below the reverse, and containing 0 settles nothing about direction. The teaching-methods interval $(2.40, 12.00)$ for A minus B lies above 0, so method A's mean runs higher by roughly 2.4 to 12.0 points. The width comes from the confidence level, the two groups' variability, and the two sample sizes, and it answers a second question: how large the plausible differences are.
An interval containing 0 gets reported as showing the two means are equal, though a 4-point gap can sit comfortably inside $(-1.2, 4.8)$ and the correct sentence is that no difference was detected. Direction gets judged by which side holds more of the interval, or read off the sign with no order of subtraction stated. Width gets read as importance, when a precise $(0.2, 0.6)$ on a 100-point exam is a real and trivial difference and a wide $(1, 40)$ is a real and unpinned one. And causation gets asserted from two intact groups that were never randomly assigned.
The work
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Lesson
Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Means
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Reads an interval for a difference of means against zero, refuses the reading that turns a straddling interval into a finding of equality, separates the width's precision from the difference's importance, and writes a four-part justification with its hedge intact.
Diagnostic
10-item topic check
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Ten items on differences of means and zero: intervals containing zero read as proof of equality, direction judged by which side holds more, signs read without the subtraction order, and narrow intervals read as large effects. Take it cold to find your habit, or after the lesson to check it is gone.
Targeted Practice
Drill a single misconception
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Pick one of the failure modes you missed and drill it on its own. The round is adaptive: two correct in a row clears it for now and moves you to the next. Two in a row is a checkpoint, not proof: if the error resurfaces later, the misconception comes back.