Data collection and processing

IB Chemistry SL· 10 min read

1. Types of Experimental Data★☆☆☆☆⏱ 2 min

📘 Definition

Experimental Data

All observations collected from practical experiments are split into two core categories based on their format.

Example:

Qualitative: observation of a yellow precipitate; Quantitative: measurement of 24.3 °C temperature

Qualitative data describes non-numerical observations: colour changes, odour, state of matter, precipitate formation, or rate of bubble production. Quantitative data is numerical, measured with laboratory equipment, and is used for all calculations in chemistry.

📐 Worked Example

Classify the following observations as qualitative or quantitative: 1) The solution turned from purple to colourless during the reaction. 2) The mass of product collected was 1.25 g. 3) The reaction gave off a pungent odour.

1
Recall that qualitative data is descriptive, while quantitative data is numerical.
2
1. Colour change is a descriptive observation with no numerical measurement:
3
$\text{Classification: Qualitative}$
4
1. Mass of product is a numerical measurement from a balance:
5
$\text{Classification: Quantitative}$
6
1. Odour is a descriptive observation:
7
$\text{Classification: Qualitative}$

Exam tip:

In internal assessment, always include relevant qualitative observations — they are required for full marks and can explain unexpected quantitative results.

2. Uncertainties in Measurements★★☆☆☆⏱ 3 min

📘 Definition

Absolute Uncertainty

\Delta x

The margin of error for a measurement. For analog devices, it equals half the smallest division. For digital devices, it equals the smallest division.

Example:

A 50 cm³ burette has 0.1 cm³ divisions, so \Delta V = \pm 0.05 cm³

Percentage uncertainty compares the uncertainty to the size of your measurement, calculated as: $Percentage Uncertainty = \frac{Absolute Uncertainty}{Measured Value} \times 100$ . For addition/subtraction, add absolute uncertainties. For multiplication/division, add percentage uncertainties.

📐 Worked Example

A student measures 10.00 cm³ of solution using a 10 cm³ volumetric pipette with absolute uncertainty ±0.02 cm³. Calculate the percentage uncertainty.

1
Identify the given values: absolute uncertainty = 0.02 cm³, measured volume = 10.00 cm³
2
Use the percentage uncertainty formula:
3
$\text{Percentage Uncertainty} = \frac{0.02}{10.00} \times 100% = 0.2%$
4
Final result: the measurement has a percentage uncertainty of 0.2%

✓ Quick check

What is the absolute uncertainty of a mass measured on a digital balance that reads to 0.01 g?
- ±0.005 g
- ±0.01 g
- ±0.1 g
- ±0.05 g
Reveal answer
±0.01 g —
Digital devices have uncertainty equal to their smallest division, so the uncertainty is ±0.01 g.

3. Classification of Experimental Errors★★★☆☆⏱ 2 min

📘 Definition

Random vs Systematic Error

Random errors cause unpredictable, inconsistent variation in repeated measurements, leading to spread around the true value. Systematic errors are consistent, repeatable errors that shift all measurements in the same direction away from the true value.

Example:

Random: inconsistent burette reading from different angles; Systematic: uncalibrated balance reads all masses 0.02 g too high

Random errors can be reduced by repeating measurements and calculating an average mean value. Systematic errors cannot be reduced by repetition, and require equipment calibration or method adjustment to fix. Common systematic errors include heat loss in enthalpy experiments and incomplete reaction of reactants.

📐 Worked Example

Classify the error in this scenario: A thermometer calibrated at 0 °C and 100 °C reads all temperatures 2 °C higher than the true value.

1
The error is consistent, all readings are shifted 2 °C higher in the same direction
2
This matches the definition of a systematic error, caused by incorrect calibration
3
Repeating measurements will not reduce this error

Exam tip:

Exam questions regularly ask to distinguish between error types, remember: repetition only fixes random error, not systematic.

4. Processing Raw Data★★☆☆☆⏱ 2 min

When processing raw data into final results, all calculated values must have the correct number of significant figures, matching the least precise original measurement. Uncertainties from raw data must be propagated through to the final result, to show the precision of your conclusion.

📐 Worked Example

Calculate the total percentage uncertainty in the molar mass of a compound, found from $M = \frac{m}{n}$ , where mass $m = 2.50 \pm 0.01$ g and moles $n = 0.025 \pm 0.0005$ mol.

1
Calculate percentage uncertainty for mass:
2
$\frac{0.01}{2.50} \times 100% = 0.4%$
3
Calculate percentage uncertainty for moles:
4
$\frac{0.0005}{0.025} \times 100% = 2%$
5
For division, add percentage uncertainties:
6
$\text{Total percentage uncertainty} = 0.4% + 2% = 2.4%$

5. Common Pitfalls

Wrong move:

Adding percentage uncertainties for addition/subtraction calculations

Why:

Uncertainty propagation rules depend on calculation type; adding percentages is only for multiplication/division

Correct move:

Add absolute uncertainties when adding or subtracting measured values

Wrong move:

Confusing random and systematic error properties

Why:

Many students mix up which error can be reduced by repetition

Correct move:

Random error = inconsistent spread, reduced by repeating trials; systematic error = consistent shift, corrected by calibration

Wrong move:

Using half the smallest division for digital device uncertainty

Why:

The half-division rule only applies to analog devices

Correct move:

For digital devices, absolute uncertainty equals the smallest division on the display

Wrong move:

Reporting final results with more significant figures than raw data

Why:

Extra significant figures falsely imply higher precision than your measurement actually has

Correct move:

Round final results to match the number of significant figures of the least precise input measurement

Wrong move:

Omitting qualitative observations from practical reports

Why:

Students often assume only quantitative data is marked, but qualitative data is required for full IA marks

Correct move:

Always record all relevant observations (colour, state, odour) alongside numerical measurements

6. Quick Reference Cheatsheet

Concept	Rule
Qualitative data	Descriptive, non-numerical observations
Quantitative data	Numerical, measured values
Absolute uncertainty (analog)	½ × smallest division
Absolute uncertainty (digital)	1 × smallest division
Percentage uncertainty	(Absolute ÷ Measured) × 100%
Add/subtract uncertainty	Add absolute uncertainties
Multiply/divide uncertainty	Add percentage uncertainties
Random error	Reduced by repeating trials
Systematic error	Corrected by equipment calibration

When this came up on past exams

AI-estimated based on syllabus patterns — cross-check with official past papers for accuracy. Use only as revision-focus signals.

2023 · 1
Uncertainty calculation question
2022 · 2
Error classification question

Going deeper

Official SyllabusIB Chemistry SL Practical SkillsOfficial practical skills guidance

What's Next

Data collection and processing is the foundation of all practical work in IB Chemistry, and these skills are assessed across both written exams and your internal assessment (IA), which makes up 20% of your final grade. Mastering uncertainty and error classification will help you critically evaluate your own experiments for the IA conclusion and evaluation section, a common area where students lose marks. These skills also build the base for processing graphical data and evaluating experimental methods.