Worksheets and research resources · Page 42

Distinguish Uncertainty in a Mean from Variation in a Future Observation

Choose an interval that matches whether the target is a population mean or a new individual measurement.

Evaluate whether a straight-line contribution is plausible without replacing continuous information with arbitrary categories.

Identify sequence, period, and within-person information before treating crossover trial outcomes as independent groups.

Distinguish logical records from physical lines and prevent embedded punctuation from shifting columns.

State the causal assumptions and target effect before accepting a software-generated adjustment set.

Specify decimal and thousands separators explicitly and test values that distinguish competing interpretations.

Translate classification counts into a decision comparison while stating the threshold’s tradeoff.

Separate repeated data entry from legitimate repeat measurements and make the retained-record rule auditable.

Use a study-family evidence table to prevent duplicate participant counts and preserve uncertain matches.

Separate evidence of a sufficiently small difference from a failure to reject a zero difference.

Distinguish observed spread, uncertainty in an estimate, and prediction variability in every figure.

Compare the validation population with the development setting before interpreting a performance difference.