Data collection and processing

IB Chemistry HL· Unit 7: Practical and investigative skills· 5 min read

1. Classification of Experimental Data★☆☆☆☆⏱ 10 min

All data collected in IB Chemistry practical work falls into one of two core categories, with different roles in analysis and assessment.

📘 Definition

Qualitative data

Descriptive, non-numerical data that records observable properties or changes during a reaction or experiment.

Example:

Observing that a blue precipitate formed, or that a reaction produced a colorless gas.

Quantitative data is numerical data obtained from measurement, and always includes a unit and an associated uncertainty.

📐 Worked Example

Classify the following observations as qualitative or quantitative: (a) The reaction temperature increased by $4.2° e x t C$ , (b) The gas produced turned limewater cloudy, (c) $0.25 g$ of calcium carbonate was added to acid.

1
(a) Temperature change is a numerical measurement with a unit →
2
quantitative
3
(b) This is a descriptive observation of a property with no numerical value →
4
qualitative
5
(c) Mass of reactant is a numerical measurement →
6
quantitative

2. Types of Experimental Error★★☆☆☆⏱ 15 min

All measurements have some degree of error, which is split into two categories with different causes and effects on results.

📘 Definition

Random error

Unpredictable variation between measurements that causes scatter around the true value, caused by uncontrollable environmental fluctuations or human reading variation.

Example:

Slight differences in reading a burette scale between repeated trials.

Systematic error is a consistent error that shifts all measurements in the same direction away from the true value, caused by faulty equipment or flawed procedure.

📐 Worked Example

Identify each error as random or systematic: (a) A balance always reads 0.02 g higher than the true mass, (b) Different students get slightly different volume readings from the same cylinder, (c) A thermometer is always 1.5 °C too low.

1
(a) All readings are shifted consistently higher, so this is → systematic error
2
(b) Unpredictable variation with no consistent shift, so this is → random error
3
(c) All readings are consistently shifted lower, so this is → systematic error

3. Calculating Uncertainty in Processed Data★★★☆☆⏱ 20 min

The absolute uncertainty of a measurement is typically half the smallest division of an analog instrument, and equal to the smallest division for a digital instrument. When processing multiple measurements, uncertainty is combined by set rules:

For addition and subtraction: add absolute uncertainties
For multiplication, division, or powers: add percentage uncertainties

📐 Worked Example

A student measures mass = $12.50 p m 0.01$ g, volume = $10.2 p m 0.1$ cm³. Calculate density and its uncertainty.

1
Step 1: Calculate mean density:
2
$\rho = \frac{m}{V} = \frac{12.50}{10.2} = 1.225\ \text{g cm}^{-3}$
3
Step 2: Calculate percentage uncertainty for each measurement:
4
$\%U_{mass} = \frac{0.01}{12.50} \times 100 = 0.08\%$
5
$\%U_{volume} = \frac{0.1}{10.2} \times 100 = 0.98\%$
6
Step 3: Add percentage uncertainties (density is division):
7
$\text{Total } \%U = 0.08 + 0.98 = 1.06\% \approx 1.1\%$
8
Step 4: Calculate absolute uncertainty:
9
$\text{Absolute uncertainty} = \frac{1.1}{100} \times 1.225 = 0.013 \approx 0.01\ \text{g cm}^{-3}$
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Step 5: Final result:
11
$\rho = 1.23 \\pm 0.01\ \text{g cm}^{-3}$

4. Significant Figure Rules★★☆☆☆⏱ 15 min

Significant figures communicate the precision of a measurement. The number of significant figures in a final result must match the precision of the least precise measurement used.

Non-zero digits are always significant
Zeros between non-zero digits are always significant
Leading zeros before the first non-zero digit are never significant
Trailing zeros after a decimal point are always significant
Trailing zeros in whole numbers are ambiguous; use scientific notation to clarify

📐 Worked Example

Calculate $2.35 g + 1.2 g$ and give the result with the correct number of significant figures.

1
Step 1: Calculate the raw sum: $2.35 + 1.2 = 3.55$
2
Step 2: The least precise measurement is $1.2$ , which has 1 decimal place
3
Step 3: Round the result to 1 decimal place → final result = $3.6 g$

5. Common Pitfalls

Wrong move:

Adding percentage uncertainties for addition/subtraction instead of adding absolute uncertainties

Why:

This leads to incorrect underestimation of total uncertainty for additive operations

Correct move:

Always add absolute uncertainties for addition and subtraction of measured values

Wrong move:

Reporting final results with more significant figures than the least precise measurement

Why:

This falsely implies higher precision than exists, leading to lost marks in IA and Paper 3

Correct move:

Always round the final result to match the precision of the least precise input measurement

Wrong move:

Confusing random error and systematic error in exam answers

Why:

Examiners require correct classification of error type to award marks for analysis questions

Correct move:

Scatter around true value = random; consistent shift in one direction = systematic

Wrong move:

Using the smallest instrument division as absolute uncertainty for analog instruments

Why:

This doubles the true uncertainty for most analog measurements, leading to wrong calculations

Correct move:

Absolute uncertainty for analog instruments is half the smallest division; it equals the smallest division for digital instruments

6. Quick Reference Cheatsheet

Concept	Core Rule
Qualitative data	Descriptive, non-numerical observation
Quantitative data	Numerical measurement with units
Random error	Unpredictable scatter, reduced by repeats
Systematic error	Consistent shift, fixed by calibration
Add/Subtract Uncertainty	Add absolute uncertainties
Multiply/Divide Uncertainty	Add percentage uncertainties
Add/Subtract Sig Figs	Round to least decimal places
Multiply/Divide Sig Figs	Round to least number of sig figs

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.

2022 · 3
Uncertainty calculation for titration
2023 · 3
Significant figure rule application
2021 · IA
Error analysis for enthalpy change

Going deeper

guideIB Chemistry Practical Assessment HandbookOfficial practical skills requirements

What's Next

Data collection and processing is the foundation of all practical work in IB Chemistry HL, and mastery of these skills directly impacts your marks for internal assessment as well as Paper 3 practical questions. These concepts build on basic measurement fundamentals, extending to rigorous error analysis required for advanced practicals like titrations, enthalpy change experiments, and rate studies. Correct application of uncertainty and significant figure rules is expected in all answers involving experimental data, even in theory papers. Building on these core processing skills, you will next learn how to interpret and process experimental data graphically.