IB Physics SL S2.2 Collecting and processing data Questions

SLSL HLHL

Practise IB Physics SL S2.2 by recording measurements, processing uncertainty, plotting data and extracting justified physical relationships.

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

Exam points & syllabus

Exam points

Test direct proportionality using a constant ratio or a straight graph through the origin; an increasing trend alone is insufficient.
Transform both the equation and plotted data consistently before assessing linearity, intercepts or a missing point.
Use the fitted intercept to determine the model parameter and a supported uncertainty, reporting appropriate significant figures.
Use the area under a power–time pulse to compare energy; peak height alone does not fully demonstrate an energy decrease.
Inspect an anomalous reading and show the chosen data and calculation for a mean, preserving the task’s allowed alternatives.
Derive a sketch from the supplied model, checking curvature and intercept instead of assuming every increasing relation is a straight line.
Compare systematic offset and gradient uncertainty separately; use the model’s predicted intercept and the spread of admissible slopes.
Justify repeat trials by reference to random uncertainty, precision or variation in the actual setup; an unexplained average is insufficient.

Covered syllabus points

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

Exam points

Compare areas under power–time pulses to compare their energies; a lower peak alone does not fully justify a claim of energy loss.
Direct proportionality needs a straight graph through the origin; an increasing trend or straight line with a nonzero intercept is insufficient.
Test proportionality with ratios from suitable separated data points, showing that the ratios differ; do not rely only on qualitative trend.
Check a transformed graph against the proposed equation, using the transformed variables and the origin condition rather than the original axes.
Use the fitted intercept to obtain the model parameter and include an uncertainty supported by admissible lines and appropriate precision.
Convert original measurements into the plotted variables and units before locating a missing point, including any square-root transformation.
Identify the anomalous measurement and show the calculation and data choice used for a mean; follow the task’s stated marking alternatives.
For PV=K, explain either approximately constant products or a straight P-versus-1/V graph whose intercept is consistent with zero.
If displayed axes hide the origin, explain how an intercept calculation or a larger-axis replot can test the proportionality claim.
Derive the sketch from the supplied equation: T²=cm+B with positive constants gives an increasing, flattening T–m curve with nonzero intercept.

[Maximum number: 3]

A group of students uses pressurized air to move a piston that forces a nail into a block of wood. A gauge is used to measure the pressure P of compressed air above atmospheric pressure. The nail enters the wood perpendicular to its surface.

The students use a ruler to measure the length of the nail which remains above the surface of the wood as shown. The depth of the nail inside the wood is d. All necessary length measurements are recorded using a ruler with uncertainty $\pm 1 \mathrm{~mm}$ .

(a)

The students systematically increase the pressure and calculate d.

[ 2 ]

(i)

By using two sets of data in the table, show that the relationship between d and P is not directly proportional.

[ 2 ]

(b)

The students suggest the following relationship between d and P :

d=k \sqrt{P}

where k is a constant.
To verify the relationship, the variation of d with $\sqrt{P}$ is plotted.
One data point is missing.

[ 1 ]

(i)

Determine the coordinates of the missing point using the original data set and plot it on the graph.

[ 1 ]

Correctly plotted ( $447 \pm 3,30$ )

Exam points

Compare systematic offset and gradient uncertainty separately: the intercept departure and extreme-gradient spread show different features.
For a model predicting proportionality, check whether an origin-passing line fits the error bars before identifying systematic error.
Explain repetitions by referring to random uncertainty or improved precision; “to find an average” or “reduce error” alone may not earn the mark.
One trial per setting cannot assess variations or anomalies well; link repeated trials to the stated sources of variation.

[Maximum number: 1]

A student is running an experiment to determine the acceleration of free-fall g. She drops a small metal ball from a given height and measures the time t taken for it to fall using an electronic timer. She repeats the same experiment several times.

Suggest a reason for repeating the experiment in the same conditions.

More syllabus points

S2.2.1—Collecting dataNo questions yet

No public sample questions are available for this syllabus point yet.

Question 1

Question (a)

Question (i)

Question (b)

Question (i)

Question 1

More syllabus points