IB Physics HL S2.2 Collecting and processing data Questions

SLSL HLHL

Practise IB Physics HL S2.2 by propagating uncertainty, transforming variables and evaluating fits in complex experimental datasets.

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

Exam points & syllabus

Exam points

Compare each dataset with the stated reference and describe how its difference changes with the independent variable.
Test proportionality with ratios or an origin-crossing line; distinguish this from merely finding a straight trend.
Use the supplied logarithmic intensity-width line with the total number of exposures to estimate the screen width.
Interpret sensitivity as a large response per unit input change, so compare gradient magnitudes rather than output values alone.
Use a stated sound speed and the echo time to find depth, accounting for the sound’s outward and return journeys.
Handle an outlier transparently before averaging, and keep enough precision to show how the chosen readings affect the result.
Complete transformed data tables from the actual inputs, using the correct logarithm and the significant figures required by the rubric.
Use fit against uncertainty bars to assess agreement; infer a systematic offset only when the stated model predicts a zero intercept.
Improve reliability with the same apparatus by averaging repeated or multi-fringe measurements, or enlarging the spacing to reduce relative uncertainty.
A constant extra delay shifts all time data equally; it changes the intercept but leaves the gradient-derived sound speed unchanged.
Support confidence in a measurement method through independent teams, instruments, peer checks or comparison with a known terrestrial reference.
Explain omitted uncertainties as negligible relative to the analysis or as precisely known inputs, without assuming zero measurement error.

Covered syllabus points

S2.2.1—Collecting data S2.2.2—Processing and interpreting data S2.2.3—Data quality

Exam points

Compare both hearing curves with the stated normal-hearing reference: A has constant loss, while B’s loss grows with frequency.
Test proportionality with the transformed variables: the given T ∝ 1/d model needs an origin-crossing line, while T versus d fails that test.
For repeated equal exposures, find the required intensity fraction 1/25000, then use the supplied ln(I/I₀)-versus-width line to obtain about 1.7 mm.
Use two non-adjacent force-pressure pairs to compare F/Δp; different ratios reject proportionality, whereas unequal increments test only straightness.
Read thermistor sensitivity from the largest magnitude of the resistance-temperature gradient; this plot is steepest at low temperatures.
Use the given approximate sound speed and the dashed-line echo time, then halve the round-trip distance: 1500 × 60 μs / 2 = 0.045 m.
Identify the 0.26 mm diameter outlier and calculate the mean transparently; the rubric accepts showing results both with and without it.
For Polaris B, calculate L/R² = 1.63 × 10⁹ and log₁₀T = 3.84 from the supplied table; both entries require three significant figures.
A fixed microphone offset adds the same delay to all measurements; the time-length graph shifts but its gradient and inferred sound speed stay unchanged.

[Maximum number: 2]

A student determines the resistivity $\rho$ of a metal that is in the form of a cylindrical wire. The student makes the following measurements:

\text { Length } L \text { of the wire }=(462 \pm 2) \mathrm{mm}

Readings for the diameter d of the wire:

\text { Resistance } R \text { of the wire }=13.7 \Omega \pm 1.5 \%

Show that the mean diameter of the wire is about 0.2 mm .

Exam points

Use the stated proportional model before diagnosing bias: a fitted line that cannot include the origin and all error bars indicates systematic error.
Judge the supplied trend line against the uncertainty bars; missing some bars is the stated evidence that the fit is imperfect.
With the double-slit apparatus, measure several fringe spacings together or repeat measurements, then average or explain the reduced percentage uncertainty.
Alternatively, increase screen distance or reduce slit separation to enlarge fringe spacing, then explain the lower percentage uncertainty.
Support confidence in stellar measurements through peer checks, comparison with terrestrial sources or corroboration by independent methods and teams.
Explain omitted uncertainties as negligible for the analysis or as precisely known inputs; their omission does not make measurements error-free.

[Maximum number: 1]

A student attaches one end of a copper wire to an oscillator operating at a fixed frequency. The other end of the wire passes over a pulley to weights that hang vertically. The first harmonic standing wave is established by using the slider to change the length of the wire. The procedure is repeated for different weights.

The mass m of the weights and the wavelength $\lambda$ of the wave are related by

m=\frac{\mu f^{2}}{g} \lambda^{2}

where $\mu$ is a constant, f is the frequency of the wave and $g=9.8 \mathrm{~ms}^{-2}$ .

The graph shows the data obtained by the student, plotted to show the variation of m with $\lambda^{2}$ .

Identify the evidence for a systematic error in the data.

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S2.2.1—Collecting dataNo questions yet

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