IB Physics SL S2.3 Concluding and evaluating Questions

SLSL HLHL

Practise IB Physics SL S2.3 by drawing evidence-based conclusions, identifying limitations and proposing realistic improvements.

21 questions in this syllabus scope, with question parts counted together.

Exam points & syllabus

Exam points

Justify agreement by comparing the experimental uncertainty interval with the accepted value; unequal central values alone do not show disagreement.
Link a conclusion to the fitted relation or obtained constant and keep it consistent with the actual preceding result.
Trace each error through the actual calculation; a common offset can cancel in a difference, ratio of differences or gradient.
Separate random uncertainty from systematic bias: averaging can reduce the former, while a persistent offset remains through repetitions.
Evaluate a model against error bars, its required origin and its tested range; convert transformed-axis limits to the requested variable.
Treat extrapolation beyond measured scales as a limitation; the relation or physical assumptions may cease to apply.
Name a concrete error mechanism in the apparatus and explain its effect, rather than giving a vague error or efficiency statement.
State heat-balance assumptions and distinguish a common-loss subtraction method from assuming no heat loss at all.
Check random scatter independently of systematic offset; close points can still form a biased trend.
Identify a physical feature omitted by the model, such as drag, friction or mass distribution, and relate it to the represented system.
A failed predicted constant product can refute an inverse-proportion hypothesis; approximately constant products support it without proving it.
Connect a timing improvement to its effect: slower motion can increase measured time and reduce percentage timing uncertainty.
Use a relevant measurement limitation to discuss different results, including difficulty measuring a very small parallax angle.
For the same caliper, suggest zeroing, averaging repeated readings or checking different diameters of the same sphere.

Covered syllabus points

S2.3.1—Concluding S2.3.2—Evaluating

Exam points

Compare the experimental uncertainty interval with the accepted value; justify agreement rather than judging unequal central values alone.
Link a conclusion to the investigated model using the fitted relation or the calculated constant’s agreement with its accepted value.
Keep the conclusion consistent with the obtained result, including a justified contrary conclusion when an earlier calculated value does not agree.

[Maximum number: 1]

A satellite powered by solar cells directed towards the Sun is in a polar orbit about the Earth.

The satellite is orbiting the Earth at a distance of 6600 km from the centre of the Earth.

Not all scientists agree that global warming is caused by the activities of man. Outline how scientists try to ensure agreement on a scientific issue.

Exam points

Trace an offset through the actual calculation: a constant shift in all current readings leaves a V–I gradient and its inferred resistance unchanged.
A common position offset cancels in position differences; show that both numerator and denominator of the supplied ratio remain unchanged.
Use the transformed graph and error bars to judge a model’s valid interval, then convert reciprocal-axis limits back to the requested variable.
A subtraction method cancels a common heat loss only when the loss is approximately the same in the compared runs.
Connect a longer measured time to lower percentage timing uncertainty when explaining the advantage of a slower experimental motion.
Question extrapolation beyond the measured range; the empirical relation or physical assumptions may fail at the new scale.
Identify a specific systematic-error mechanism in the apparatus, such as pulley friction or an omitted tray mass, rather than a vague “measurement error”.
For timing one pendulum oscillation manually, identify reaction time at the stopwatch’s start or stop as the main source of timing error.
Distinguish scatter from offset when comparing random and systematic errors; a tightly grouped trend can still show a substantial bias.
State concrete heat-balance assumptions, such as negligible heat exchange with the room, negligible container heating or negligible evaporation.
Judge a trend line against the uncertainty bars, rather than requiring it to pass through every measured point’s centre.
Specify a real physical feature omitted by a model, such as friction, drag or the represented mass distribution.
A clear failure of the predicted constant product can refute an inverse-proportion hypothesis; near-constant products support it without proving it.
Explain repetitions by linking averaging to reduced random uncertainty in the measured quantity and the final derived result.
Repeated measurements do not remove a systematic bias that persists in every reading; distinguish this from reducing random uncertainty.
Explain disagreement between measured results with a relevant uncertainty source; very small parallax angles can be difficult to measure accurately.
Improve the same-caliper procedure by zeroing it, averaging repeated readings or measuring different diameters of the same sphere.
In heater-to-water calorimetry, identify where input energy goes besides warming the water and explain why neglecting that loss overestimates c.

[Maximum number: 1]

The graph shows the plotted data for this experiment. Uncertainties in the data are not shown

State one reason why the results of the experiment could not be used to predict the natural frequency of oscillation for girders of height 50 m .

Question 1

Question 1