Author: eduKate Asia
-
PSLE Science Reality Lab Vol No.074 | “It Survived the Drop Test” — Were Both Products Dropped the Same Way?
A Primary 5/6 Reality Lab guide to evaluating dramatic product drop tests. Learn to separate a demonstration from a fair comparison by checking drop height, surface, orientation, starting condition, repeats and the outcome that was actually measured.
-
PSLE Science Reality Lab Vol No.073 | “Both Lines Start at 100” — Were Their Real Starting Values the Same?
A Primary 5/6 Reality Lab guide to charts that rebase different measurements to 100. Learn to separate relative change from original absolute level, recover the base period and avoid reading equal index values as equal real quantities.
-
PSLE Science Reality Lab Vol No.072 | “Removes 99%” — 99% of What Was There at the Start, and What Is Left?
A Primary 5/6 Reality Lab guide to percentage-removal claims: identify the starting quantity, match before-and-after measurements, calculate what remains, inspect test conditions and avoid confusing a high percentage with zero remaining.
-
PSLE Science Reality Lab Vol No.071 | “Tested for 10,000 Cycles” — Does an Accelerated Test Prove Years of Normal Use?
A Primary 5/6 Reality Lab guide to accelerated durability claims: how to separate test cycles from ordinary-use lifetime, inspect added stresses, compare failure modes and decide how far a laboratory result can travel.
-
PSLE Science Reality Lab Vol No.070 | “Three Sensors Agree” — Can They All Share the Same Bias?
A Primary 5/6 Reality Lab guide to a subtle evidence trap: several sensors can agree closely and still share the same measurement bias. Learn how to check agreement, references, calibration and shared conditions before treating consensus as correctness.
-
The Tutor Handbook Vol No.0107 | The Peer Baseline Trap — How a Tutor Uses Three Learners Together Without Letting the Fastest Student Become the Standard for Everyone Else
THB-0107. The Peer Baseline Trap explains how tutors preserve the benefits of three-learner tuition while judging each learner against the task, the learning goal and their own evidence rather than turning the fastest peer into the hidden standard.
